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@@ -7,3 +7,9 @@ target-eclipse
*~
Python/notebooks/.ipynb_checkpoints/
+nesp/*.html
+nesp/.ipynb_checkpoints
+nesp/__pycache__
+nesp/.venv
+nesp/build
+nesp/nesp.egg-info
\ No newline at end of file
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@@ -0,0 +1 @@
+3.12
diff --git a/NESP/OffshoreRenewable_Energy_Infrastructure_Regions.zip b/NESP/OffshoreRenewable_Energy_Infrastructure_Regions.zip
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diff --git a/NESP/README.md b/NESP/README.md
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+++ b/NESP/README.md
@@ -0,0 +1,15 @@
+# Intro
+
+## Pre-requisites
+Python package and environment manager. We highly recommend [uv](https://docs.astral.sh/uv/#installation).
+
+## Usage
+1. Navigate to the NESP directory of the repo
+
+2. Create and activate the python environment
+
+```bash
+uv venv \
+&& source .venv/bin/activate \
+&& uv pip install .
+```
\ No newline at end of file
diff --git a/NESP/amsa.ipynb b/NESP/amsa.ipynb
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@@ -0,0 +1,699 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# AMSA Vessel Tracking: Comprehensive Strategic Analysis (2012–2025)\n",
+ "\n",
+ "| | |\n",
+ "| ----------- | ----------- |\n",
+ "| Author:| Karishma Khanna |\n",
+ "| Edited:| 2026-01-08 |\n",
+ "| Affiliation:| AODN/IMOS |\n",
+ "| e-mail:| karishma.khanna@utas.edu.au |\n",
+ "| Date of creation:| 2026-01-08 |\n",
+ "| Date of last update:| 2026-01-08 |\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "This is a template notebook outlining the library stack and content structure for NESP Data Products.\n",
+ "\n",
+ "It outlines:\n",
+ "1. [Connecting to NESP 5.9 datasets](#connecting-to-nesp-59-datasets)\n",
+ "2. [H3 Aggregation and Mapping](#h3-aggregation-and-mapping)\n",
+ "3. [Identifying Trend Sites](#identifying-trend-sites)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## NESP 5.9 Datasets\n",
+ "\n",
+ "| Dataset name | S3 URL |\n",
+ "| ------------ | ------ |\n",
+ "| AMSA | [link](s3://data-uplift-public/stored/datauplift/amsa/) |\n",
+ "| Kelp | [link](s3://data-uplift-public/stored/datauplift/kelp/kelp.parquet) |\n",
+ "| NRMN | [link](s3://data-uplift-public/stored/datauplift/nrmn/nrmn.parquet) |\n",
+ "| Seabird | [link](s3://data-uplift-public/stored/datauplift/seabird/seabird.parquet) |\n",
+ "| Seagrass | [link](s3://data-uplift-public/stored/datauplift/seagrass/seagrass.parquet) |\n",
+ "\n",
+ "### S3 Details\n",
+ "NESP 5.9 datasets are currently stored in S3 and are publicly available as per the following table:\n",
+ "\n",
+ "| Bucket | Key | Partitioned |\n",
+ "| -------------------- | -------------------------------------------------------- | ----------- |\n",
+ "| `data-uplift-public` | `stored/datauplift/amsa/year=*/source=*/*.parquet` | [x] |\n",
+ "| `data-uplift-public` | `stored/datauplift/kelp/kelp.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/nrmn/nrmn.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seabird/seabird.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seagrass/seagrass.parquet` | [ ] |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Required Python & Packages\n",
+ "The required packages are found in the [pyproject.toml](pyproject.toml) under `dependencies`.\n",
+ "\n",
+ "### `uv` installation\n",
+ "It is highly recommended to manage python version, environment and projects with [uv](https://github.com/astral-sh/uv).\n",
+ "\n",
+ "```bash\n",
+ "uv venv && source .venv/bin/activate && uv pip install .\n",
+ "```\n",
+ "\n",
+ "### `pip` installation\n",
+ "Otherwise, native python tooling can be used to manage the python environment and project. \n",
+ "\n",
+ "*Note that python3.12 or greater is required.*\n",
+ "\n",
+ "```bash\n",
+ "python -m venv .venv && source .venv/bin/activate && pip install .\n",
+ "```\n",
+ "\n",
+ "### Running Notebooks in Jupyter or IDE\n",
+ "The notebook can be manually run by launching jupyter notebook from the console:\n",
+ "```bash\n",
+ "jupyter notebook\n",
+ "```\n",
+ "Otherwise, if running in an IDE, select the newly created `.venv` as the kernal.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## Notebook Stack\n",
+ "\n",
+ "### IO\n",
+ "`pyarrow` is the defacto parquet and arrow standard library tooling in the python eco-system, offering exceptional performance, documentation and stability.\n",
+ "\n",
+ "Its `fs` class allows connecting to parquet datasets in AWS S3, which is perfect for our use case.\n",
+ "\n",
+ "### Computation\n",
+ "`polars` is fast replacing pandas as the primary python dataframe API. It is more tightly integrated with arrow (and pyarrow), has exceptional performance, greater than memory dataset manipulation, a more consistent and declarative API, built in plotting and many other advantages.\n",
+ "\n",
+ "### Mapping\n",
+ "`pydeck` is a python binding for `deck.gl`, a GPU-powered framework for visual exploratory data analysis of large datasets. It is optimised for Jupyter notebooks and highly customisable.\n",
+ "\n",
+ "Standard mapping tools like `folium` struggle to handle the number of mapped shapes, so we favor the more optimised `pydeck` library.\n",
+ "\n",
+ "### Plotting\n",
+ "`seaborn` and `matplotlib` are used for statistical data visualization. `seaborn` provides a high-level interface for drawing attractive and informative statistical graphics, while `matplotlib` manages the figure layouts and axis formatting.\n",
+ "\n",
+ "### Rich\n",
+ "TODO: Update on `rich` library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set up rich console\n",
+ "import rich\n",
+ "import rich.console\n",
+ "console = rich.console.Console()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "## Connecting to NESP 5.9 Datasets\n",
+ "\n",
+ "NESP 5.9 datasets can be connected to via a pyarrow `S3FileSystem`.\n",
+ "\n",
+ "The `S3FileSystem` manages the connection to the NESP 5.9 datasets stored in S3, resulting in a `pyarrow.Dataset` handle that we may use for efficient loading and subsetting of the dataset.\n",
+ "\n",
+ "Note that a dataset can be comprised of a single parquet file or partitioned (into separate parquet fragments/files).\n",
+ "\n",
+ "This step also makes the dataset schema available and file structure available allowing for pre-filtering without downloading the entire dataset.\n",
+ "\n",
+ "Finally, we may estimate the in memory dataset size by sampling the dataset. If the dataset is small, we may use a `DataFrame` straight away. Otherwise if the dataset is too large, we must pre-aggregate and or filter using a `LazyFrame` before loading the dataset into memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyarrow\n",
+ "import pyarrow.fs\n",
+ "import pyarrow.dataset\n",
+ "import util\n",
+ "\n",
+ "# --- FileSystem Configuration ---\n",
+ "# Initialize S3FileSystem for ap-southeast-2 region.\n",
+ "# Set anonymous=True for public bucket access.\n",
+ "\n",
+ "FILE_SYSTEM = pyarrow.fs.S3FileSystem(\n",
+ " region=\"ap-southeast-2\", \n",
+ " anonymous=True,\n",
+ ")\n",
+ "\n",
+ "# --- Dataset Connection ---\n",
+ "# Create a PyArrow Dataset handle for the AMSA source.\n",
+ "# This performs a lazy connection; data is not loaded into memory yet.\n",
+ "ds = pyarrow.dataset.dataset(\n",
+ " source=\"data-uplift-public/stored/datauplift/amsa/dataset\",\n",
+ " filesystem=FILE_SYSTEM,\n",
+ ")\n",
+ "\n",
+ "# --- Schema Inspection ---\n",
+ "\n",
+ "console.log(\"📂 Inspecting AMSA Archive...\")\n",
+ "rich.print('Total Inspected files: ',len(list(ds.get_fragments())))\n",
+ "\n",
+ "# Display schema definition and units.\n",
+ "schema_rich_table = util.generate_schema_rich_table(\n",
+ " schema=ds.schema,\n",
+ " metadata_keys=[\n",
+ " \"definition\",\n",
+ " \"unit\"\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "console.print(schema_rich_table)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "## Loading Data\n",
+ "The dataset handle is our connection to data in S3. We can use this handle to load data directly into a `DataFrame` or `LazyFrame`\n",
+ "\n",
+ "### DataFrame\n",
+ "For smaller datasets the `DataFrame` is preferrable. This loads the entire dataset into memory ready for analysis.\n",
+ "\n",
+ "### LazyFrame\n",
+ "For larger datasets the `DataFrame` may not fit into memory. In these cases the `LazyFrame` is ideal as it allows the dataset to be filtered and or aggregated prior to loading the dataset, allowing it to fit into memory, ready for analysis.\n",
+ "\n",
+ "> **For AMSA:** Given the dataset exceeds available RAM, we initialize a `LazyFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import polars as pl\n",
+ "import polars_h3\n",
+ "import polars \n",
+ "import os\n",
+ "\n",
+ "# --- Initialize LazyFrame ---\n",
+ "# scan_pyarrow_dataset wraps the existing dataset handle.\n",
+ "# No data is transferred until action (collect) is called.\n",
+ "amsa_lf = pl.scan_pyarrow_dataset(ds)\n",
+ "\n",
+ "print(\"✅ LazyFrame initialized.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "## H3 Aggregation and Mapping\n",
+ "\n",
+ "The NESP 5.9 datasets are aggregated data products that cover large regions and time periods. It is useful to first aggregate the datasets so that we may understand their temporal-spatial bounds.\n",
+ "\n",
+ "### H3 Indexing\n",
+ "The `h3` library allows us to spatially index datasets at different [resolutions](https://h3geo.org/docs/core-library/restable#average-area-in-km2) (zoom levels) according to the record latitude and longitude.\n",
+ "\n",
+ "Additionally, the `polars_h3` library offers a polars style interface for faster tagging with `polars.DataFrames`.\n",
+ "\n",
+ "### H3 Mapping\n",
+ "The `pydeck` library offers a built in [`H3HexagonLayer`](https://deck.gl/docs/api-reference/geo-layers/h3-hexagon-layer) layer type that greatly reduces complexity in mapping H3 Shapes.\n",
+ "\n",
+ "> **Visualization Strategy:**\n",
+ "> * **Log-Scaling:** Maritime traffic density varies wildly (Power Law). We apply a $Log_{10}$ transform to make both ports and open oceans visible.\n",
+ "> * **Global Max:** We calculate the maximum density across *all years* to create a fixed color scale. This ensures that \"Red\" in 2013 represents the same density as \"Red\" in 2024, allowing for valid comparisons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "console.log(\"🚀 Starting H3 Aggregation...\")\n",
+ "\n",
+ "# --- H3 Aggregation ---\n",
+ "# Group by the pre-calculated Resolution 8 Index and Year.\n",
+ "h3_trend_df = (\n",
+ " amsa_lf.group_by(\n",
+ " polars.col(\"h3Index\").alias(\"h3IndexResolution8\"),\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"n_records\"),\n",
+ " )\n",
+ " # The collect statement applies the above aggregation and streams\n",
+ " # only the required data to the client (us)\n",
+ " .collect(engine=\"streaming\")\n",
+ ")\n",
+ "\n",
+ "console.log(f\"✅ Aggregation Complete.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "## Visualising the H3 Aggregation\n",
+ "\n",
+ "With the data aggregated and the global scale determined, we can now render the spatial density map using `pydeck`.\n",
+ "\n",
+ "The `generate_comparative_map` function below handles:\n",
+ "1. **Filtering:** Selecting the specific year from the aggregated dataset.\n",
+ "2. **Normalization:** Mapping the Log-Score to a fixed color palette using the `GLOBAL_MAX_LOG` calculated previously.\n",
+ "3. **Rendering:** Generating the 3D Hexagon Layer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydeck\n",
+ "\n",
+ "def generate_comparative_map(dataframe, target_year):\n",
+ " \"\"\"\n",
+ " Generates a pydeck H3 Hexagon map for a specific year using a fixed global color scale.\n",
+ "\n",
+ " This function filters a Polars DataFrame for a specific year, bins the data into \n",
+ " discrete color buckets based on a global maximum value to ensure visual consistency \n",
+ " across different time slices, and renders the result using pydeck.\n",
+ "\n",
+ " :param dataframe: The input data containing H3 indices and record counts.\n",
+ " :type dataframe: polars.LazyFrame or polars.DataFrame\n",
+ " :param target_year: The specific year to filter and visualize.\n",
+ " :type target_year: int\n",
+ " :return: A pydeck Deck object rendered in the current environment.\n",
+ " :rtype: pydeck.bindings.deck.Deck\n",
+ " \"\"\"\n",
+ " \n",
+ " console.log(f\"🎨 Generating Map for {target_year}...\")\n",
+ "\n",
+ " # --- Filter Data ---\n",
+ " # Filter the LazyFrame for the target year.\n",
+ " year_df = dataframe.filter(\n",
+ " polars.col(\"year\").eq(target_year),\n",
+ " )\n",
+ "\n",
+ " # # --- Color Palette ---\n",
+ " # # Generate a 10-step discrete color palette ('plasma') for high contrast.\n",
+ " n_quantiles = 10\n",
+ "\n",
+ " # --- Layer Construction ---\n",
+ " # Create a separate H3HexagonLayer for each color bucket.\n",
+ " # This is required because pydeck layers typically take a single fill color.\n",
+ " layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=year_df,\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " hexagon_index_column_name=\"h3IndexResolution8\",\n",
+ " n_quantiles=n_quantiles,\n",
+ " color_palette=\"plasma\",\n",
+ " )\n",
+ "\n",
+ " # --- View State & Deck ---\n",
+ " # Dynamically center view as per data\n",
+ " view_state = util.generate_view_state(\n",
+ " df=year_df.with_columns(\n",
+ " polars_h3.cell_to_lat(polars.col(\"h3IndexResolution8\")).alias(\"decimalLatitude\"),\n",
+ " polars_h3.cell_to_lng(polars.col(\"h3IndexResolution8\")).alias(\"decimalLongitude\"),\n",
+ " ),\n",
+ " )\n",
+ " \n",
+ " r = pydeck.Deck(\n",
+ " layers=layers,\n",
+ " initial_view_state=view_state,\n",
+ " map_style=pydeck.map_styles.CARTO_LIGHT, # White background\n",
+ " tooltip={\n",
+ " \"html\": \"H3: {h3IndexResolution8}
Pings: {n_records}
\"\n",
+ " }\n",
+ " )\n",
+ " \n",
+ " return r.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The map for year 2013\n",
+ "print(\"--- 2013 Vessel Density ---\")\n",
+ "generate_comparative_map(h3_trend_df, 2013)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The map for year 2019\n",
+ "print(\"--- 2019 Vessel Density ---\")\n",
+ "generate_comparative_map(h3_trend_df, 2019)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The map for year 2024\n",
+ "print(\"--- 2024 Vessel Density ---\")\n",
+ "generate_comparative_map(h3_trend_df, 2024)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "## Regional Analysis: Sydney Harbour\n",
+ "\n",
+ "While the global map uses a coarse resolution (Resolution 8, approx 0.7km²) suitable for open ocean shipping lanes, complex waterways like Sydney Harbour require finer detail.\n",
+ "\n",
+ "To analyze this region effectively, we perform a **Spatial Deep Dive**:\n",
+ "1. **Spatial Filter:** We restrict the query to the Sydney bounding box.\n",
+ "2. **Re-Indexing:** We calculate a new H3 Index at **Resolution 9** (approx 0.1km²). This allows us to resolve individual ferry routes and harbour traffic that would otherwise be merged into a single block."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# To minimise memory footprint, load a where and when df\n",
+ "temporal_spatial_df = amsa_lf.select(\n",
+ " polars.col(\"eventDate\"),\n",
+ " polars.col(\"decimalLatitude\"),\n",
+ " polars.col(\"decimalLongitude\"),\n",
+ ").collect()\n",
+ "temporal_spatial_df.estimated_size(unit=\"gb\")\n",
+ "temporal_spatial_df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_high_res_map(\n",
+ " df: polars.DataFrame,\n",
+ " target_year: int,\n",
+ " h3_resolution: int = 9,\n",
+ " max_lat: float = -33.78,\n",
+ " min_lat: float = -33.93,\n",
+ " max_lon: float = 151.35,\n",
+ " min_lon: float = 151.10,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Generate a high resolution h3 map.mro\n",
+ "\n",
+ " Defaults set up for Sydney.\n",
+ " \"\"\"\n",
+ " \n",
+ " console.log(f\"⚓ Generating High-Res Map (Res {h3_resolution}) for {target_year}...\")\n",
+ "\n",
+ " # Error check the bounds\n",
+ " if max_lat <= min_lat:\n",
+ " raise ValueError(\"max_lat must be greater than min_lat: found `{max_lat}` <= `{min_lat}`\")\n",
+ " if max_lon <= min_lon:\n",
+ " raise ValueError(\"max_lon must be greater than min_lat: found `{max_lon}` <= `{min_lon}`\")\n",
+ "\n",
+ " # --- Filter & Re-Index (Resolution 9) ---\n",
+ " # We filter the main LazyFrame ('amsa_lf') to the specific region and year.\n",
+ " # We calculate a new 'h3Index' at the specified new h3 resolution.\n",
+ " map_df = (\n",
+ " df\n",
+ " .filter(\n",
+ " polars.col(\"decimalLatitude\").is_between(min_lat, max_lat),\n",
+ " polars.col(\"decimalLongitude\").is_between(min_lon, max_lon),\n",
+ " polars.col(\"eventDate\").dt.year().eq(target_year),\n",
+ " )\n",
+ " .select([\"decimalLatitude\", \"decimalLongitude\"])\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"), \n",
+ " lng=polars.col(\"decimalLongitude\"), \n",
+ " resolution=h3_resolution,\n",
+ " return_dtype=polars.String\n",
+ " ).alias(\"h3Index\")\n",
+ " )\n",
+ " .group_by(\"h3Index\")\n",
+ " .agg(polars.len().alias(\"n_records\"))\n",
+ " )\n",
+ " \n",
+ " # --- Validation ---\n",
+ " if map_df.height == 0:\n",
+ " console.log(\"⚠️ No data found for this year/region.\")\n",
+ " return\n",
+ "\n",
+ " n_quantiles = 10\n",
+ " layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=map_df,\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " hexagon_index_column_name=\"h3Index\",\n",
+ " n_quantiles=n_quantiles,\n",
+ " color_palette=\"plasma\",\n",
+ " )\n",
+ "\n",
+ " # --- View State & Deck ---\n",
+ " # Dynamically center view as per data\n",
+ " view_state = util.generate_view_state(\n",
+ " df=map_df.with_columns(\n",
+ " polars_h3.cell_to_lat(polars.col(\"h3Index\")).alias(\"decimalLatitude\"),\n",
+ " polars_h3.cell_to_lng(polars.col(\"h3Index\")).alias(\"decimalLongitude\"),\n",
+ " ),\n",
+ " )\n",
+ " \n",
+ " r = pydeck.Deck(\n",
+ " layers=layers,\n",
+ " initial_view_state=view_state,\n",
+ " map_style=pydeck.map_styles.CARTO_LIGHT,\n",
+ " tooltip={\"text\": \"n_records: {n_records}\"}\n",
+ " )\n",
+ " \n",
+ " return r.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The map for year 2016\n",
+ "print(\"--- 2016 Vessel Density Map of Sydney Harbour---\")\n",
+ "generate_high_res_map(temporal_spatial_df, 2016, h3_resolution=12)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The map for year 2024\n",
+ "print(\"--- 2016 Vessel Density Map of Sydney Harbour---\")\n",
+ "generate_high_res_map(temporal_spatial_df, 2024, h3_resolution=12)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22",
+ "metadata": {},
+ "source": [
+ "## Longitudinal Data Health\n",
+ "\n",
+ "This section characterizes the dataset's **temporal stability**.\n",
+ "\n",
+ "Long-term maritime datasets are frequently influenced by **\"Structural Breaks\"**—step-changes in data volume caused by upgrades to surveillance infrastructure (e.g., new satellite launches). The following analysis distinguishes between **actual fleet growth** (increased traffic) and **systematic growth** (improved sensor sensitivity).\n",
+ "\n",
+ "### Methodology\n",
+ "To quantify these trends, we aggregate the data annually and derive three core metrics:\n",
+ "1. **Volume:** The total count of AIS transmission records (Data Size).\n",
+ "2. **Fleet:** The count of unique vessels identified by `craftID` (Traffic Size).\n",
+ "3. **Intensity:** The average number of records per vessel (Sensor Resolution).\n",
+ "\n",
+ "> **Normalization Strategy:** To compare trends across different scales (thousands of ships vs. millions of records), all metrics are indexed to their **2012 baseline (2012 = 100)**. This allows for a direct correlation analysis of relative growth rates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "\n",
+ "# --- Aggregation ---\n",
+ "# We reuse the 'amsa_lf' LazyFrame initialized above and group by Year to extract high-level statistics without loading the raw data.\n",
+ "\n",
+ "console.log(\"🚀 Calculating Annual Statistics (Streaming Mode)...\")\n",
+ "\n",
+ "yearly_stats = (\n",
+ " amsa_lf.select([\"eventDate\", \"craftID\"])\n",
+ " .with_columns(pl.col(\"eventDate\").dt.year().alias(\"Year\"))\n",
+ " .group_by(\"Year\")\n",
+ " .agg([\n",
+ " pl.col(\"craftID\").n_unique().alias(\"Unique_Vessels\"), # Fleet Size\n",
+ " pl.len().alias(\"Total_Records\"), # Data Volume\n",
+ " pl.col(\"eventDate\").n_unique().alias(\"Unique_Timestamps\") # Temporal Granularity\n",
+ " ])\n",
+ " .sort(\"Year\")\n",
+ " .collect(streaming=True) # Trigger computation\n",
+ ")\n",
+ "\n",
+ "# --- Normalization (Base 100) ---\n",
+ "# To compare \"Vessels\" (thousands) vs \"Records\" (millions), we index them to their 2012 baseline. (2012 value = 100).\n",
+ "\n",
+ "base_vessels = yearly_stats[\"Unique_Vessels\"][0]\n",
+ "base_records = yearly_stats[\"Total_Records\"][0]\n",
+ "base_timestamps = yearly_stats[\"Unique_Timestamps\"][0]\n",
+ "\n",
+ "yearly_stats = yearly_stats.with_columns([\n",
+ " (pl.col(\"Total_Records\") / pl.col(\"Unique_Vessels\")).alias(\"Records_Per_Vessel\"),\n",
+ " (pl.col(\"Unique_Vessels\") / base_vessels * 100).alias(\"Idx_Vessels\"),\n",
+ " (pl.col(\"Total_Records\") / base_records * 100).alias(\"Idx_Records\"),\n",
+ " (pl.col(\"Unique_Timestamps\") / base_timestamps * 100).alias(\"Idx_Timestamps\"),\n",
+ " # Calculate Ping Gap (Inverse of Frequency) for annotation\n",
+ " (365 * 24 * 60 / pl.col(\"Unique_Timestamps\")).alias(\"Avg_Gap_Minutes\")\n",
+ "])\n",
+ "\n",
+ "# --- Visualization ---\n",
+ "# We use Matplotlib to create a 2x2 Grid, and Seaborn to paint the data.\n",
+ "\n",
+ "sns.set_theme(style=\"whitegrid\")\n",
+ "df_plot = yearly_stats.to_pandas() # Convert small agg stats to Pandas for plotting\n",
+ "\n",
+ "# Function to format Y-axis as Millions\n",
+ "def millions(x, pos):\n",
+ " return '%1.1fM' % (x * 1e-6)\n",
+ "\n",
+ "sns.set_theme(style=\"whitegrid\")\n",
+ "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+ "plt.subplots_adjust(hspace=0.3)\n",
+ "\n",
+ "# A. Volume (Bar Chart)\n",
+ "sns.barplot(data=df_plot, x=\"Year\", y=\"Total_Records\", ax=axes[0,0], color=\"#3498db\", alpha=0.7)\n",
+ "axes[0,0].yaxis.set_major_formatter(FuncFormatter(millions))\n",
+ "axes[0,0].set_title(\"A. Total Data Volume (Records)\", fontweight='bold')\n",
+ "axes[0,0].grid(axis='y', alpha=0.3)\n",
+ "\n",
+ "# B. Fleet Size (Line Chart)\n",
+ "sns.lineplot(data=df_plot, x=\"Year\", y=\"Unique_Vessels\", marker=\"o\", ax=axes[0,1], color=\"#e74c3c\", linewidth=3)\n",
+ "axes[0,1].set_title(\"B. Unique Vessels\", fontweight='bold')\n",
+ "axes[0,1].grid(True, alpha=0.3)\n",
+ "\n",
+ "# C. Growth Correlation (Multi-Line)\n",
+ "# This graph reveals if data grew because *ships* increased or *sensors* improved.\n",
+ "ax_c = axes[1,0]\n",
+ "sns.lineplot(data=df_plot, x=\"Year\", y=\"Idx_Vessels\", ax=ax_c, color=\"#e74c3c\", label=\"Vessel Growth\", linewidth=2.5, marker=\"o\")\n",
+ "sns.lineplot(data=df_plot, x=\"Year\", y=\"Idx_Records\", ax=ax_c, color=\"#3498db\", label=\"Volume Growth (Records)\", linewidth=2.5, linestyle=\"--\")\n",
+ "sns.lineplot(data=df_plot, x=\"Year\", y=\"Idx_Timestamps\", ax=ax_c, color=\"#2ecc71\", label=\"Frequency Growth (Timestamps)\", linewidth=2.5, linestyle=\"-.\")\n",
+ "\n",
+ "ax_c.set_ylabel(\"Growth Index (Base = 100)\")\n",
+ "ax_c.set_title(\"C. Correlation: Vessels vs. Volume vs. Frequency\", fontweight='bold')\n",
+ "ax_c.legend(loc='upper left')\n",
+ "\n",
+ "# Add \"Ping Gap\" Annotation\n",
+ "if len(df_plot) > 0:\n",
+ " gap_start = df_plot[\"Avg_Gap_Minutes\"].iloc[0]\n",
+ " gap_end = df_plot[\"Avg_Gap_Minutes\"].iloc[-1]\n",
+ " last_year = df_plot[\"Year\"].iloc[-1]\n",
+ " last_val = df_plot[\"Idx_Timestamps\"].iloc[-1]\n",
+ " \n",
+ " ax_c.annotate(f\"Ping Gap improved:\\n{gap_start:.1f}m -> {gap_end:.1f}m\", \n",
+ " xy=(last_year, last_val), \n",
+ " xytext=(last_year - 4, last_val + 50), \n",
+ " arrowprops=dict(facecolor='black', shrink=0.05),\n",
+ " fontsize=10, bbox=dict(boxstyle=\"round,pad=0.4\", fc=\"white\", ec=\"gray\", alpha=0.9))\n",
+ "\n",
+ "# D. Intensity (Bar Chart)\n",
+ "sns.barplot(data=df_plot, x=\"Year\", y=\"Records_Per_Vessel\", ax=axes[1,1], color=\"#9b59b6\", alpha=0.6)\n",
+ "axes[1,1].set_title(\"D. Tracking Intensity (Avg Records per Vessel)\", fontweight='bold')\n",
+ "axes[1,1].grid(axis='y', alpha=0.3)\n",
+ "\n",
+ "plt.suptitle(\"Longitudinal Data Growth & System Resolution Analysis\", fontsize=18, y=0.95)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NESP/h3.md b/NESP/h3.md
new file mode 100644
index 0000000..522b655
--- /dev/null
+++ b/NESP/h3.md
@@ -0,0 +1,73 @@
+# Slide 1: Introduction to Uber H3
+## The World in Hexagons: Introduction to Uber H3
+**A Discrete Global Grid System for Geospatial Indexing**
+
+---
+
+### What it is
+An open-source, hierarchical hexagonal indexing system developed by Uber to manage and analyze massive spatial datasets.
+
+### The Problem
+Traditional Latitude/Longitude pairs are hard to "group" or "join" quickly. Additionally, administrative boundaries (like Marine Regions) are highly detailed not well suited to efficient dataset analysis.
+
+### The Solution
+H3 tiles the entire Earth’s surface with hexagons, turning coordinates into unique **64-bit identifiers** for lightning-fast lookups.
+
+---
+
+# Slide 2: Why Hexagons?
+## The Geometry of Efficiency
+
+* **Equidistance:** In a square grid, diagonal neighbors are further away than edge neighbors. In a hexagonal grid, all 6 neighbors are the same distance from the center.
+* **Reduced Distortion:** Hexagons better approximate the Earth's curvature than squares or triangles.
+* **Smoothing:** Hexagons provide a more natural representation for heatmapping.
+
+---
+
+# Slide 3: Hierarchical Resolution
+## Scaling from Continents to Coffee Tables
+
+H3 supports **16 levels of detail**, allowing for seamless transitions between global and local views.
+
+| Resolution | Average Area ($km^2$) | Usage Example |
+| :--- | :--- | :--- |
+| **0** | ~4.4 Million | Continental Analysis |
+| **7** | ~5.16 | Harbour or Bay Analysis |
+| **15** | ~0.0000009 | Individual Sample Analysis |
+
+* **Aperture 7:** Each "parent" hexagon contains approximately 7 "child" hexagons.
+
+---
+
+# Slide 4: Key Technical Features
+## Built for Performance
+
+* **Fast Joins:** Instead of complex "Point-in-Polygon" math, H3 treats spatial joins as simple integer comparisons.
+* **Compactness:** Entire datasets can be "compacted" (using a few large hexagons instead of thousands of small ones) to save storage and reduce lookups.
+* **Interoperability:** Written in C with bindings for Python, Java, JavaScript, and more.
+
+---
+
+# Slide 5: Real-World Applications
+## Uber H3 in Action
+
+> "H3 allows us to treat the physical world as a searchable, indexable database."
+
+# Slide 5: Real-World Applications
+## Optimization: Heat Mapping & Shape Tagging
+
+---
+
+### 1. High-Performance Heat Mapping
+* **Fixed-Bin Normalization:** Traditional heat maps often use arbitrary radii that cause visual overlap distortion. H3 provides a uniform grid where each cell acts as a precise, non-overlapping bucket.
+* **Database-Level Aggregation:** Aggregate millions of points into Hex IDs using simple `GROUP BY` SQL statements. This moves the heavy lifting to the database, making front-end rendering instantaneous.
+
+### 2. Efficient Shape Tagging (Spatial Joins)
+* **The "Point-in-Polygon" Killer:** Replaces expensive trigonometric calculations with simple 64-bit integer comparisons.
+* **Polyfill Precision:** Represent complex, irregular boundaries (geofences, delivery zones) as a set of H3 indexes.
+
+---
+
+### Key Engineering Benefits
+* **Storage:** Store a small set of compression friendly locality sensitive integers instead of complex GeoJSON or WKT strings.
+* **Scalability:** Move from $O(N^2)$ spatial intersections to $O(1)$ or $O(N)$ index-based joins.
\ No newline at end of file
diff --git a/NESP/pyproject.toml b/NESP/pyproject.toml
new file mode 100644
index 0000000..c40a057
--- /dev/null
+++ b/NESP/pyproject.toml
@@ -0,0 +1,26 @@
+[project]
+name = "nesp"
+version = "1.0.0"
+description = "Load and analyse NESP 5.9 Datasets"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+ "altair>=6.0.0",
+ "geopandas>=1.1.2",
+ "h3>=4.4.2",
+ "matplotlib>=3.10.8",
+ "notebook>=7.5.3",
+ "pandas==2.3.3",
+ "polars>=1.37.1",
+ "polars-h3>=0.6.1",
+ "polars-st>=0.4.3",
+ "pyarrow>=23.0.0",
+ "pydeck>=0.9.1",
+ "rich>=14.3.1",
+ "seaborn>=0.13.2",
+]
+
+[dependency-groups]
+dev = [
+ "nbstripout>=0.9.0",
+]
diff --git a/NESP/requirements.txt b/NESP/requirements.txt
new file mode 100644
index 0000000..fd02891
--- /dev/null
+++ b/NESP/requirements.txt
@@ -0,0 +1,924 @@
+# This file was autogenerated by uv via the following command:
+# uv export --format requirements.txt -o requirements.txt
+anyio==4.12.1 \
+ --hash=sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703 \
+ --hash=sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c
+ # via
+ # httpx
+ # jupyter-server
+appnope==0.1.4 ; sys_platform == 'darwin' \
+ --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \
+ --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c
+ # via ipykernel
+argon2-cffi==25.1.0 \
+ --hash=sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1 \
+ --hash=sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741
+ # via jupyter-server
+argon2-cffi-bindings==25.1.0 \
+ --hash=sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99 \
+ --hash=sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6 \
+ --hash=sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44 \
+ --hash=sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a \
+ --hash=sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f \
+ --hash=sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2 \
+ --hash=sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0 \
+ --hash=sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f \
+ --hash=sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623 \
+ --hash=sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b \
+ --hash=sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44 \
+ --hash=sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98 \
+ --hash=sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500 \
+ --hash=sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94 \
+ --hash=sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6 \
+ --hash=sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d \
+ --hash=sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85 \
+ --hash=sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92 \
+ --hash=sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d \
+ --hash=sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a \
+ --hash=sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb
+ # via argon2-cffi
+arrow==1.4.0 \
+ --hash=sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205 \
+ --hash=sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7
+ # via isoduration
+asttokens==3.0.1 \
+ --hash=sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a \
+ --hash=sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7
+ # via stack-data
+async-lru==2.1.0 \
+ --hash=sha256:9eeb2fecd3fe42cc8a787fc32ead53a3a7158cc43d039c3c55ab3e4e5b2a80ed \
+ --hash=sha256:fa12dcf99a42ac1280bc16c634bbaf06883809790f6304d85cdab3f666f33a7e
+ # via jupyterlab
+attrs==25.4.0 \
+ --hash=sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11 \
+ --hash=sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373
+ # via
+ # jsonschema
+ # referencing
+babel==2.17.0 \
+ --hash=sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d \
+ --hash=sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2
+ # via jupyterlab-server
+beautifulsoup4==4.14.3 \
+ --hash=sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb \
+ --hash=sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86
+ # via nbconvert
+bleach==6.3.0 \
+ --hash=sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22 \
+ --hash=sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6
+ # via nbconvert
+certifi==2026.1.4 \
+ --hash=sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c \
+ --hash=sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120
+ # via
+ # httpcore
+ # httpx
+ # pyogrio
+ # requests
+cffi==2.0.0 \
+ --hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \
+ --hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \
+ --hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \
+ --hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \
+ --hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \
+ --hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \
+ --hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \
+ --hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \
+ --hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \
+ --hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \
+ --hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \
+ --hash=sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1 \
+ --hash=sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529 \
+ --hash=sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca \
+ --hash=sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4 \
+ --hash=sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b \
+ --hash=sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205 \
+ --hash=sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27 \
+ --hash=sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512 \
+ --hash=sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d \
+ --hash=sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c \
+ --hash=sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8 \
+ --hash=sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9 \
+ --hash=sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775 \
+ --hash=sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc \
+ --hash=sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13 \
+ --hash=sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26 \
+ --hash=sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b \
+ --hash=sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6 \
+ --hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \
+ --hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \
+ --hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \
+ --hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \
+ --hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \
+ --hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5
+ # via
+ # argon2-cffi-bindings
+ # pyzmq
+charset-normalizer==3.4.4 \
+ --hash=sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152 \
+ --hash=sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72 \
+ --hash=sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e \
+ --hash=sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c \
+ --hash=sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2 \
+ --hash=sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44 \
+ --hash=sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede \
+ --hash=sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed \
+ --hash=sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133 \
+ --hash=sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e \
+ --hash=sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14 \
+ --hash=sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828 \
+ --hash=sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f \
+ --hash=sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328 \
+ --hash=sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090 \
+ --hash=sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c \
+ --hash=sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb \
+ --hash=sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a \
+ --hash=sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec \
+ --hash=sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc \
+ --hash=sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac \
+ --hash=sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 \
+ --hash=sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14 \
+ --hash=sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1 \
+ --hash=sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3 \
+ --hash=sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e \
+ --hash=sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6 \
+ --hash=sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191 \
+ --hash=sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd \
+ --hash=sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2 \
+ --hash=sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794 \
+ --hash=sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 \
+ --hash=sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490 \
+ --hash=sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9
+ # via requests
+colorama==0.4.6 ; sys_platform == 'win32' \
+ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+ # via ipython
+comm==0.2.3 \
+ --hash=sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971 \
+ --hash=sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417
+ # via ipykernel
+debugpy==1.8.19 \
+ --hash=sha256:0601708223fe1cd0e27c6cce67a899d92c7d68e73690211e6788a4b0e1903f5b \
+ --hash=sha256:1e8c4d1bd230067bf1bbcdbd6032e5a57068638eb28b9153d008ecde288152af \
+ --hash=sha256:360ffd231a780abbc414ba0f005dad409e71c78637efe8f2bd75837132a41d38 \
+ --hash=sha256:85016a73ab84dea1c1f1dcd88ec692993bcbe4532d1b49ecb5f3c688ae50c606 \
+ --hash=sha256:8e19a725f5d486f20e53a1dde2ab8bb2c9607c40c00a42ab646def962b41125f \
+ --hash=sha256:91e35db2672a0abaf325f4868fcac9c1674a0d9ad9bb8a8c849c03a5ebba3e6d \
+ --hash=sha256:b605f17e89ba0ecee994391194285fada89cee111cfcd29d6f2ee11cbdc40976 \
+ --hash=sha256:c30639998a9f9cd9699b4b621942c0179a6527f083c72351f95c6ab1728d5b73 \
+ --hash=sha256:d40c016c1f538dbf1762936e3aeb43a89b965069d9f60f9e39d35d9d25e6b809 \
+ --hash=sha256:eea7e5987445ab0b5ed258093722d5ecb8bb72217c5c9b1e21f64efe23ddebdb
+ # via ipykernel
+decorator==5.2.1 \
+ --hash=sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360 \
+ --hash=sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a
+ # via ipython
+defusedxml==0.7.1 \
+ --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \
+ --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61
+ # via nbconvert
+executing==2.2.1 \
+ --hash=sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4 \
+ --hash=sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017
+ # via stack-data
+fastjsonschema==2.21.2 \
+ --hash=sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463 \
+ --hash=sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de
+ # via nbformat
+fqdn==1.5.1 \
+ --hash=sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f \
+ --hash=sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014
+ # via jsonschema
+h11==0.16.0 \
+ --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \
+ --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86
+ # via httpcore
+httpcore==1.0.9 \
+ --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \
+ --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8
+ # via httpx
+httpx==0.28.1 \
+ --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \
+ --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad
+ # via jupyterlab
+idna==3.11 \
+ --hash=sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea \
+ --hash=sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902
+ # via
+ # anyio
+ # httpx
+ # jsonschema
+ # requests
+ipykernel==7.1.0 \
+ --hash=sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db \
+ --hash=sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c
+ # via jupyterlab
+ipython==9.9.0 \
+ --hash=sha256:48fbed1b2de5e2c7177eefa144aba7fcb82dac514f09b57e2ac9da34ddb54220 \
+ --hash=sha256:b457fe9165df2b84e8ec909a97abcf2ed88f565970efba16b1f7229c283d252b
+ # via ipykernel
+ipython-pygments-lexers==1.1.1 \
+ --hash=sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81 \
+ --hash=sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c
+ # via ipython
+isoduration==20.11.0 \
+ --hash=sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9 \
+ --hash=sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042
+ # via jsonschema
+jedi==0.19.2 \
+ --hash=sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0 \
+ --hash=sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9
+ # via ipython
+jinja2==3.1.6 \
+ --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \
+ --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67
+ # via
+ # jupyter-server
+ # jupyterlab
+ # jupyterlab-server
+ # nbconvert
+json5==0.13.0 \
+ --hash=sha256:9a08e1dd65f6a4d4c6fa82d216cf2477349ec2346a38fd70cc11d2557499fbcc \
+ --hash=sha256:b1edf8d487721c0bf64d83c28e91280781f6e21f4a797d3261c7c828d4c165bf
+ # via jupyterlab-server
+jsonpointer==3.0.0 \
+ --hash=sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942 \
+ --hash=sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef
+ # via jsonschema
+jsonschema==4.26.0 \
+ --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \
+ --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce
+ # via
+ # jupyter-events
+ # jupyterlab-server
+ # nbformat
+jsonschema-specifications==2025.9.1 \
+ --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \
+ --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d
+ # via jsonschema
+jupyter-client==8.8.0 \
+ --hash=sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e \
+ --hash=sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a
+ # via
+ # ipykernel
+ # jupyter-server
+ # nbclient
+jupyter-core==5.9.1 \
+ --hash=sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508 \
+ --hash=sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407
+ # via
+ # ipykernel
+ # jupyter-client
+ # jupyter-server
+ # jupyterlab
+ # nbclient
+ # nbconvert
+ # nbformat
+jupyter-events==0.12.0 \
+ --hash=sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb \
+ --hash=sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b
+ # via jupyter-server
+jupyter-lsp==2.3.0 \
+ --hash=sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245 \
+ --hash=sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f
+ # via jupyterlab
+jupyter-server==2.17.0 \
+ --hash=sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5 \
+ --hash=sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f
+ # via
+ # jupyter-lsp
+ # jupyterlab
+ # jupyterlab-server
+ # notebook
+ # notebook-shim
+jupyter-server-terminals==0.5.4 \
+ --hash=sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14 \
+ --hash=sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5
+ # via jupyter-server
+jupyterlab==4.5.3 \
+ --hash=sha256:4a159f71067cb38e4a82e86a42de8e7e926f384d7f2291964f282282096d27e8 \
+ --hash=sha256:63c9f3a48de72ba00df766ad6eed416394f5bb883829f11eeff0872302520ba7
+ # via notebook
+jupyterlab-pygments==0.3.0 \
+ --hash=sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d \
+ --hash=sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780
+ # via nbconvert
+jupyterlab-server==2.28.0 \
+ --hash=sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c \
+ --hash=sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968
+ # via
+ # jupyterlab
+ # notebook
+jupytext==1.19.1 \
+ --hash=sha256:82587c07e299173c70ed5e8ec7e75183edf1be289ed518bab49ad0d4e3d5f433 \
+ --hash=sha256:d8975035155d034bdfde5c0c37891425314b7ea8d3a6c4b5d18c294348714cd9
+ # via nesp
+lark==1.3.1 \
+ --hash=sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905 \
+ --hash=sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12
+ # via rfc3987-syntax
+markdown-it-py==4.0.0 \
+ --hash=sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147 \
+ --hash=sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3
+ # via
+ # jupytext
+ # mdit-py-plugins
+markupsafe==3.0.3 \
+ --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \
+ --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \
+ --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \
+ --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \
+ --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \
+ --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \
+ --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \
+ --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \
+ --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \
+ --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \
+ --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \
+ --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \
+ --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \
+ --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \
+ --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \
+ --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \
+ --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \
+ --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \
+ --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \
+ --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \
+ --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \
+ --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \
+ --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \
+ --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \
+ --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \
+ --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \
+ --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \
+ --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \
+ --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \
+ --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \
+ --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \
+ --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \
+ --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \
+ --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \
+ --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \
+ --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \
+ --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \
+ --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \
+ --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \
+ --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \
+ --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \
+ --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \
+ --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \
+ --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \
+ --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50
+ # via
+ # jinja2
+ # nbconvert
+matplotlib-inline==0.2.1 \
+ --hash=sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76 \
+ --hash=sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe
+ # via
+ # ipykernel
+ # ipython
+mdit-py-plugins==0.5.0 \
+ --hash=sha256:07a08422fc1936a5d26d146759e9155ea466e842f5ab2f7d2266dd084c8dab1f \
+ --hash=sha256:f4918cb50119f50446560513a8e311d574ff6aaed72606ddae6d35716fe809c6
+ # via jupytext
+mdurl==0.1.2 \
+ --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+ --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+ # via markdown-it-py
+mistune==3.2.0 \
+ --hash=sha256:708487c8a8cdd99c9d90eb3ed4c3ed961246ff78ac82f03418f5183ab70e398a \
+ --hash=sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1
+ # via nbconvert
+nbclient==0.10.4 \
+ --hash=sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9 \
+ --hash=sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440
+ # via nbconvert
+nbconvert==7.16.6 \
+ --hash=sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b \
+ --hash=sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582
+ # via jupyter-server
+nbformat==5.10.4 \
+ --hash=sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a \
+ --hash=sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b
+ # via
+ # jupyter-server
+ # jupytext
+ # nbclient
+ # nbconvert
+nest-asyncio==1.6.0 \
+ --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \
+ --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c
+ # via ipykernel
+notebook==7.5.3 \
+ --hash=sha256:393ceb269cf9fdb02a3be607a57d7bd5c2c14604f1818a17dbeb38e04f98cbfa \
+ --hash=sha256:c997bfa1a2a9eb58c9bbb7e77d50428befb1033dd6f02c482922e96851d67354
+ # via nesp
+notebook-shim==0.2.4 \
+ --hash=sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef \
+ --hash=sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb
+ # via
+ # jupyterlab
+ # notebook
+numpy==2.4.1 \
+ --hash=sha256:178de8f87948163d98a4c9ab5bee4ce6519ca918926ec8df195af582de28544d \
+ --hash=sha256:20d4649c773f66cc2fc36f663e091f57c3b7655f936a4c681b4250855d1da8f5 \
+ --hash=sha256:2302dc0224c1cbc49bb94f7064f3f923a971bfae45c33870dcbff63a2a550505 \
+ --hash=sha256:2f44de05659b67d20499cbc96d49f2650769afcb398b79b324bb6e297bfe3844 \
+ --hash=sha256:2ffd257026eb1b34352e749d7cc1678b5eeec3e329ad8c9965a797e08ccba205 \
+ --hash=sha256:382ad67d99ef49024f11d1ce5dcb5ad8432446e4246a4b014418ba3a1175a1f4 \
+ --hash=sha256:3869ea1ee1a1edc16c29bbe3a2f2a4e515cc3a44d43903ad41e0cacdbaf733dc \
+ --hash=sha256:3d1a100e48cb266090a031397863ff8a30050ceefd798f686ff92c67a486753d \
+ --hash=sha256:423797bdab2eeefbe608d7c1ec7b2b4fd3c58d51460f1ee26c7500a1d9c9ee93 \
+ --hash=sha256:42d7dd5fa36d16d52a84f821eb96031836fd405ee6955dd732f2023724d0aa01 \
+ --hash=sha256:49e792ec351315e16da54b543db06ca8a86985ab682602d90c60ef4ff4db2a9c \
+ --hash=sha256:4e53170557d37ae404bf8d542ca5b7c629d6efa1117dac6a83e394142ea0a43f \
+ --hash=sha256:529050522e983e00a6c1c6b67411083630de8b57f65e853d7b03d9281b8694d2 \
+ --hash=sha256:52b5f61bdb323b566b528899cc7db2ba5d1015bda7ea811a8bcf3c89c331fa42 \
+ --hash=sha256:5b55aa56165b17aaf15520beb9cbd33c9039810e0d9643dd4379e44294c7303e \
+ --hash=sha256:5d558123217a83b2d1ba316b986e9248a1ed1971ad495963d555ccd75dcb1556 \
+ --hash=sha256:5de60946f14ebe15e713a6f22850c2372fa72f4ff9a432ab44aa90edcadaa65a \
+ --hash=sha256:62fea415f83ad8fdb6c20840578e5fbaf5ddd65e0ec6c3c47eda0f69da172510 \
+ --hash=sha256:6436cffb4f2bf26c974344439439c95e152c9a527013f26b3577be6c2ca64295 \
+ --hash=sha256:69e7419c9012c4aaf695109564e3387f1259f001b4326dfa55907b098af082d3 \
+ --hash=sha256:71abbea030f2cfc3092a0ff9f8c8fdefdc5e0bf7d9d9c99663538bb0ecdac0b9 \
+ --hash=sha256:727c6c3275ddefa0dc078524a85e064c057b4f4e71ca5ca29a19163c607be745 \
+ --hash=sha256:79e9e06c4c2379db47f3f6fc7a8652e7498251789bf8ff5bd43bf478ef314ca2 \
+ --hash=sha256:7d5d7999df434a038d75a748275cd6c0094b0ecdb0837342b332a82defc4dc4d \
+ --hash=sha256:82c55962006156aeef1629b953fd359064aa47e4d82cfc8e67f0918f7da3344f \
+ --hash=sha256:8f085da926c0d491ffff3096f91078cc97ea67e7e6b65e490bc8dcda65663be2 \
+ --hash=sha256:9171a42fcad32dcf3fa86f0a4faa5e9f8facefdb276f54b8b390d90447cff4e2 \
+ --hash=sha256:92a0e65272fd60bfa0d9278e0484c2f52fe03b97aedc02b357f33fe752c52ffb \
+ --hash=sha256:941c2a93313d030f219f3a71fd3d91a728b82979a5e8034eb2e60d394a2b83f9 \
+ --hash=sha256:98b35775e03ab7f868908b524fc0a84d38932d8daf7b7e1c3c3a1b6c7a2c9f15 \
+ --hash=sha256:a1ceafc5042451a858231588a104093474c6a5c57dcc724841f5c888d237d690 \
+ --hash=sha256:a73044b752f5d34d4232f25f18160a1cc418ea4507f5f11e299d8ac36875f8a0 \
+ --hash=sha256:a7870e8c5fc11aef57d6fea4b4085e537a3a60ad2cdd14322ed531fdca68d261 \
+ --hash=sha256:c0faba4a331195bfa96f93dd9dfaa10b2c7aa8cda3a02b7fd635e588fe821bf5 \
+ --hash=sha256:ce9ce141a505053b3c7bce3216071f3bf5c182b8b28930f14cd24d43932cd2df \
+ --hash=sha256:d3e3087f53e2b4428766b54932644d148613c5a595150533ae7f00dab2f319a8 \
+ --hash=sha256:d797454e37570cfd61143b73b8debd623c3c0952959adb817dd310a483d58a1b \
+ --hash=sha256:e3bd2cb07841166420d2fa7146c96ce00cb3410664cbc1a6be028e456c4ee220 \
+ --hash=sha256:e7b6b5e28bbd47b7532698e5db2fe1db693d84b58c254e4389d99a27bb9b8f6b \
+ --hash=sha256:e867df947d427cdd7a60e3e271729090b0f0df80f5f10ab7dd436f40811699c3 \
+ --hash=sha256:f0a90aba7d521e6954670550e561a4cb925713bd944445dbe9e729b71f6cabee \
+ --hash=sha256:f93bc6892fe7b0663e5ffa83b61aab510aacffd58c16e012bb9352d489d90cb7 \
+ --hash=sha256:fb1461c99de4d040666ca0444057b06541e5642f800b71c56e6ea92d6a853a0c
+ # via pyogrio
+packaging==26.0 \
+ --hash=sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4 \
+ --hash=sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529
+ # via
+ # ipykernel
+ # jupyter-events
+ # jupyter-server
+ # jupyterlab
+ # jupyterlab-server
+ # jupytext
+ # nbconvert
+ # pyogrio
+pandocfilters==1.5.1 \
+ --hash=sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e \
+ --hash=sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc
+ # via nbconvert
+parso==0.8.5 \
+ --hash=sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a \
+ --hash=sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887
+ # via jedi
+pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32' \
+ --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \
+ --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f
+ # via ipython
+platformdirs==4.5.1 \
+ --hash=sha256:61d5cdcc6065745cdd94f0f878977f8de9437be93de97c1c12f853c9c0cdcbda \
+ --hash=sha256:d03afa3963c806a9bed9d5125c8f4cb2fdaf74a55ab60e5d59b3fde758104d31
+ # via jupyter-core
+polars==1.37.1 \
+ --hash=sha256:0309e2a4633e712513401964b4d95452f124ceabf7aec6db50affb9ced4a274e \
+ --hash=sha256:377fed8939a2f1223c1563cfabdc7b4a3d6ff846efa1f2ddeb8644fafd9b1aff
+ # via
+ # nesp
+ # polars-st
+polars-h3==0.6.1 \
+ --hash=sha256:0dba767a4249888e3e8518e64f741e20e41c872906518877e13a8b6688b758c5 \
+ --hash=sha256:40fabd8b3000123a6205b96cd762db6c0c9ae3ea7a9a4d6bcc906712a7982c51 \
+ --hash=sha256:8dd82314cf1370f5b72ec172f7e7cdfe867e574c0afe13121df95e938ec8e1df \
+ --hash=sha256:aed1f8582b38fe260d32889edea26b51bce1d45eb502b40f8e58dc395b9b053d \
+ --hash=sha256:bf934567e6a348c4f695989b0ec437e3a94c977e2f7f517a71b8f685a0b84ce3 \
+ --hash=sha256:f6127576be2a6cff1544ebdb896668111f869c89ad19bb37f31d5f4b043654be \
+ --hash=sha256:f6861cc9dad6b3bdd44a704770cf29aea88b1f7061d6b328e04471df53009db7
+ # via nesp
+polars-runtime-32==1.37.1 \
+ --hash=sha256:04f5d5a2f013dca7391b7d8e7672fa6d37573a87f1d45d3dd5f0d9b5565a4b0f \
+ --hash=sha256:0b8d4d73ea9977d3731927740e59d814647c5198bdbe359bcf6a8bfce2e79771 \
+ --hash=sha256:55f2c4847a8d2e267612f564de7b753a4bde3902eaabe7b436a0a4abf75949a0 \
+ --hash=sha256:68779d4a691da20a5eb767d74165a8f80a2bdfbde4b54acf59af43f7fa028d8f \
+ --hash=sha256:a8362d11ac5193b994c7e9048ffe22ccfb976699cfbf6e128ce0302e06728894 \
+ --hash=sha256:c682bf83f5f352e5e02f5c16c652c48ca40442f07b236f30662b22217320ce76 \
+ --hash=sha256:da3d3642ae944e18dd17109d2a3036cb94ce50e5495c5023c77b1599d4c861bc \
+ --hash=sha256:fbfde7c0ca8209eeaed546e4a32cca1319189aa61c5f0f9a2b4494262bd0c689 \
+ --hash=sha256:fc82b5bbe70ca1a4b764eed1419f6336752d6ba9fc1245388d7f8b12438afa2c
+ # via polars
+polars-st==0.4.3 \
+ --hash=sha256:06bfbeb6958196e742b5ddfa6da605948525d2f5e8b5b23929a3ebe8769c1e95 \
+ --hash=sha256:4436b49e2a30a6cb26831623e56f407a014e33630c5bc29db29f6397285b3907 \
+ --hash=sha256:7d4c1cbc53a1e1cc947111614f4ff5d463d73acc11cf276408fec59798e940d3 \
+ --hash=sha256:b4e6a0ed8b5ff23fd9d8b0af682935a294fea38ef5bc1ea123648845c97bc0b9 \
+ --hash=sha256:c20af615a7cb0483cd0d482ea921e26a3a3512162e52cd5cee19404e97874cf6 \
+ --hash=sha256:ca599f943e07d6cc12f7a1a57a426e8735386c62db24535edec9901071841512
+ # via nesp
+prometheus-client==0.24.1 \
+ --hash=sha256:150db128af71a5c2482b36e588fc8a6b95e498750da4b17065947c16070f4055 \
+ --hash=sha256:7e0ced7fbbd40f7b84962d5d2ab6f17ef88a72504dcf7c0b40737b43b2a461f9
+ # via jupyter-server
+prompt-toolkit==3.0.52 \
+ --hash=sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855 \
+ --hash=sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955
+ # via ipython
+psutil==7.2.1 \
+ --hash=sha256:05cc68dbb8c174828624062e73078e7e35406f4ca2d0866c272c2410d8ef06d1 \
+ --hash=sha256:08a2f175e48a898c8eb8eace45ce01777f4785bc744c90aa2cc7f2fa5462a266 \
+ --hash=sha256:0d67c1822c355aa6f7314d92018fb4268a76668a536f133599b91edd48759442 \
+ --hash=sha256:2ceae842a78d1603753561132d5ad1b2f8a7979cb0c283f5b52fb4e6e14b1a79 \
+ --hash=sha256:35630d5af80d5d0d49cfc4d64c1c13838baf6717a13effb35869a5919b854cdf \
+ --hash=sha256:3fce5f92c22b00cdefd1645aa58ab4877a01679e901555067b1bd77039aa589f \
+ --hash=sha256:494c513ccc53225ae23eec7fe6e1482f1b8a44674241b54561f755a898650679 \
+ --hash=sha256:5e38404ca2bb30ed7267a46c02f06ff842e92da3bb8c5bfdadbd35a5722314d8 \
+ --hash=sha256:81442dac7abfc2f4f4385ea9e12ddf5a796721c0f6133260687fec5c3780fa49 \
+ --hash=sha256:923f8653416604e356073e6e0bccbe7c09990acef442def2f5640dd0faa9689f \
+ --hash=sha256:93f3f7b0bb07711b49626e7940d6fe52aa9940ad86e8f7e74842e73189712129 \
+ --hash=sha256:99a4cd17a5fdd1f3d014396502daa70b5ec21bf4ffe38393e152f8e449757d67 \
+ --hash=sha256:ab2b98c9fc19f13f59628d94df5cc4cc4844bc572467d113a8b517d634e362c6 \
+ --hash=sha256:b1b0671619343aa71c20ff9767eced0483e4fc9e1f489d50923738caf6a03c17 \
+ --hash=sha256:b2e953fcfaedcfbc952b44744f22d16575d3aa78eb4f51ae74165b4e96e55f42 \
+ --hash=sha256:ba9f33bb525b14c3ea563b2fd521a84d2fa214ec59e3e6a2858f78d0844dd60d \
+ --hash=sha256:cfbe6b40ca48019a51827f20d830887b3107a74a79b01ceb8cc8de4ccb17b672 \
+ --hash=sha256:d34d2ca888208eea2b5c68186841336a7f5e0b990edec929be909353a202768a \
+ --hash=sha256:ea46c0d060491051d39f0d2cff4f98d5c72b288289f57a21556cc7d504db37fc \
+ --hash=sha256:f7583aec590485b43ca601dd9cea0dcd65bd7bb21d30ef4ddbf4ea6b5ed1bdd3 \
+ --hash=sha256:f78baafb38436d5a128f837fab2d92c276dfb48af01a240b861ae02b2413ada8
+ # via ipykernel
+ptyprocess==0.7.0 ; os_name != 'nt' or (sys_platform != 'emscripten' and sys_platform != 'win32') \
+ --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \
+ --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220
+ # via
+ # pexpect
+ # terminado
+pure-eval==0.2.3 \
+ --hash=sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0 \
+ --hash=sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42
+ # via stack-data
+pyarrow==23.0.0 \
+ --hash=sha256:075c29aeaa685fd1182992a9ed2499c66f084ee54eea47da3eb76e125e06064c \
+ --hash=sha256:0800cc58a6d17d159df823f87ad66cefebf105b982493d4bad03ee7fab84b993 \
+ --hash=sha256:14de7d48052cf4b0ed174533eafa3cfe0711b8076ad70bede32cf59f744f0d7c \
+ --hash=sha256:15a414f710dc927132dd67c361f78c194447479555af57317066ee5116b90e9e \
+ --hash=sha256:180e3150e7edfcd182d3d9afba72f7cf19839a497cc76555a8dce998a8f67615 \
+ --hash=sha256:18ec84e839b493c3886b9b5e06861962ab4adfaeb79b81c76afbd8d84c7d5fda \
+ --hash=sha256:20b187ed9550d233a872074159f765f52f9d92973191cd4b93f293a19efbe377 \
+ --hash=sha256:36d1b5bc6ddcaff0083ceec7e2561ed61a51f49cce8be079ee8ed406acb6fdef \
+ --hash=sha256:3a7c68c722da9bb5b0f8c10e3eae71d9825a4b429b40b32709df5d1fa55beb3d \
+ --hash=sha256:3e0d2e6915eca7d786be6a77bf227fbc06d825a75b5b5fe9bcbef121dec32685 \
+ --hash=sha256:427deac1f535830a744a4f04a6ac183a64fcac4341b3f618e693c41b7b98d2b0 \
+ --hash=sha256:4292b889cd224f403304ddda8b63a36e60f92911f89927ec8d98021845ea21be \
+ --hash=sha256:4b317ea6e800b5704e5e5929acb6e2dc13e9276b708ea97a39eb8b345aa2658b \
+ --hash=sha256:54810f6e6afc4ffee7c2e0051b61722fbea9a4961b46192dcfae8ea12fa09059 \
+ --hash=sha256:5b86bb649e4112fb0614294b7d0a175c7513738876b89655605ebb87c804f861 \
+ --hash=sha256:799965a5379589510d888be3094c2296efd186a17ca1cef5b77703d4d5121f53 \
+ --hash=sha256:7a7d067c9a88faca655c71bcc30ee2782038d59c802d57950826a07f60d83c4c \
+ --hash=sha256:832141cc09fac6aab1cd3719951d23301396968de87080c57c9a7634e0ecd068 \
+ --hash=sha256:a244279f240c81f135631be91146d7fa0e9e840e1dfed2aba8483eba25cd98e6 \
+ --hash=sha256:ad96a597547af7827342ffb3c503c8316e5043bb09b47a84885ce39394c96e00 \
+ --hash=sha256:ae7f30f898dfe44ea69654a35c93e8da4cef6606dc4c72394068fd95f8e9f54a \
+ --hash=sha256:b9edf990df77c2901e79608f08c13fbde60202334a4fcadb15c1f57bf7afee43 \
+ --hash=sha256:bd5556c24622df90551063ea41f559b714aa63ca953db884cfb958559087a14e \
+ --hash=sha256:c4692e83e42438dba512a570c6eaa42be2f8b6c0f492aea27dec54bdc495103a \
+ --hash=sha256:ce9486e0535a843cf85d990e2ec5820a47918235183a5c7b8b97ed7e92c2d47d \
+ --hash=sha256:dfd9e133e60eaa847fd80530a1b89a052f09f695d0b9c34c235ea6b2e0924cf7 \
+ --hash=sha256:e438dd3f33894e34fd02b26bd12a32d30d006f5852315f611aa4add6c7fab4bc \
+ --hash=sha256:ebc017d765d71d80a3f8584ca0566b53e40464586585ac64176115baa0ada7d3 \
+ --hash=sha256:ef7cac8fe6fccd8b9e7617bfac785b0371a7fe26af59463074e4882747145d40
+ # via
+ # nesp
+ # polars-st
+pycparser==3.0 ; implementation_name != 'PyPy' \
+ --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \
+ --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992
+ # via cffi
+pygments==2.19.2 \
+ --hash=sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887 \
+ --hash=sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b
+ # via
+ # ipython
+ # ipython-pygments-lexers
+ # nbconvert
+pyogrio==0.12.1 \
+ --hash=sha256:0622bc1a186421547660271083079b38d42e6f868802936d8538c0b379f1ab6b \
+ --hash=sha256:1511b39a283fa27cda906cd187a791578942a87a40b6a06697d9b43bb8ac80b0 \
+ --hash=sha256:207bd60c7ffbcea84584596e3637653aa7095e9ee20fa408f90c7f9460392613 \
+ --hash=sha256:2c50345b382f1be801d654ec22c70ee974d6057d4ba7afe984b55f2192bc94ee \
+ --hash=sha256:5399f66730978d8852ef5f44dbafa0f738e7f28f4f784349f36830b69a9d2134 \
+ --hash=sha256:58042584f3fd4cabb0f55d26c1405053f656be8a5c266c38140316a1e981aca0 \
+ --hash=sha256:5881017f29e110d3613819667657844d8e961b747f2d35cf92f273c27af6d068 \
+ --hash=sha256:5a1b0453d1c9e7b03715dd57296c8f3790acb8b50d7e3b5844b3074a18f50709 \
+ --hash=sha256:648c6f7f5f214d30e6cf493b4af1d59782907ac068af9119ca35f18153d6865a \
+ --hash=sha256:73a88436f9962750d782853727897ac2722cac5900d920e39fab3e56d7a6a7f1 \
+ --hash=sha256:806f620e0c54b54dbdd65e9b6368d24f344cda84c9343364b40a57eb3e1c4dca \
+ --hash=sha256:9fe7286946f35a73e6370dc5855bc7a5e8e7babf9e4a8bad7a3279a1d94c7ea9 \
+ --hash=sha256:a0643e041dee3e8e038fce69f52a915ecb486e6d7b674c0f9919f3c9e9629689 \
+ --hash=sha256:b438e38e4ccbaedaa5cb5824ff5de5539315d9b2fde6547c1e816576924ee8ca \
+ --hash=sha256:b4c888fc08f388be4dd99dfca5e84a5cdc5994deeec0230cc45144d3460e2b21 \
+ --hash=sha256:b5d248a0d59fe9bbf9a35690b70004c67830ee0ebe7d4f7bb8ffd8659f684b3a \
+ --hash=sha256:d3f1a19f63bfd1d3042e45f37ad1d6598123a5a604b6c4ba3f38b419273486cd \
+ --hash=sha256:e486cd6aa9ea8a15394a5f84e019d61ec18f257eeeb642348bd68c3d1e57280b \
+ --hash=sha256:e548ab705bb3e5383693717de1e6c76da97f3762ab92522cb310f93128a75ff1 \
+ --hash=sha256:e7ee560422239dd09ca7f8284cc8483a8919c30d25f3049bb0249bff4c38dec4 \
+ --hash=sha256:ec0e47a5a704e575092b2fd5c83fa0472a1d421e590f94093eb837bb0a11125d \
+ --hash=sha256:f0db95765ac0ca935c7fe579e29451294e3ab19c317b0c59c31fbe92a69155e0 \
+ --hash=sha256:f1d8d8a2fea3781dc2a05982c050259261ebc0f6c5e03732d6d79d582adf9363 \
+ --hash=sha256:f3dcc59b3316b8a0f59346bcc638a4d69997864a4d21da839192f50c4c92369a \
+ --hash=sha256:fc882779075982b93064b3bf3d8642514a6df00d9dd752493b104817072cfb01
+ # via polars-st
+python-dateutil==2.9.0.post0 \
+ --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
+ --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
+ # via
+ # arrow
+ # jupyter-client
+python-json-logger==4.0.0 \
+ --hash=sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2 \
+ --hash=sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f
+ # via jupyter-events
+pywinpty==3.0.2 ; os_name == 'nt' \
+ --hash=sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004 \
+ --hash=sha256:18f78b81e4cfee6aabe7ea8688441d30247b73e52cd9657138015c5f4ee13a51 \
+ --hash=sha256:28297cecc37bee9f24d8889e47231972d6e9e84f7b668909de54f36ca785029a \
+ --hash=sha256:34b55ae9a1b671fe3eae071d86618110538e8eaad18fcb1531c0830b91a82767 \
+ --hash=sha256:663383ecfab7fc382cc97ea5c4f7f0bb32c2f889259855df6ea34e5df42d305b
+ # via
+ # jupyter-server
+ # jupyter-server-terminals
+ # terminado
+pyyaml==6.0.3 \
+ --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \
+ --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \
+ --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \
+ --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \
+ --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \
+ --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \
+ --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \
+ --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \
+ --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \
+ --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \
+ --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \
+ --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \
+ --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \
+ --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \
+ --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \
+ --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \
+ --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \
+ --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \
+ --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \
+ --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \
+ --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \
+ --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \
+ --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \
+ --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \
+ --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \
+ --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \
+ --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \
+ --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \
+ --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6
+ # via
+ # jupyter-events
+ # jupytext
+pyzmq==27.1.0 \
+ --hash=sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d \
+ --hash=sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05 \
+ --hash=sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28 \
+ --hash=sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea \
+ --hash=sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0 \
+ --hash=sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113 \
+ --hash=sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92 \
+ --hash=sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd \
+ --hash=sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233 \
+ --hash=sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31 \
+ --hash=sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc \
+ --hash=sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd \
+ --hash=sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e \
+ --hash=sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128 \
+ --hash=sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96 \
+ --hash=sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f \
+ --hash=sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c \
+ --hash=sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2 \
+ --hash=sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146 \
+ --hash=sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97 \
+ --hash=sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5 \
+ --hash=sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf \
+ --hash=sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540 \
+ --hash=sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db \
+ --hash=sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39 \
+ --hash=sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a \
+ --hash=sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a \
+ --hash=sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856 \
+ --hash=sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9 \
+ --hash=sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7 \
+ --hash=sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7 \
+ --hash=sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496 \
+ --hash=sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6
+ # via
+ # ipykernel
+ # jupyter-client
+ # jupyter-server
+referencing==0.37.0 \
+ --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \
+ --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8
+ # via
+ # jsonschema
+ # jsonschema-specifications
+ # jupyter-events
+requests==2.32.5 \
+ --hash=sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6 \
+ --hash=sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf
+ # via jupyterlab-server
+rfc3339-validator==0.1.4 \
+ --hash=sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b \
+ --hash=sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa
+ # via
+ # jsonschema
+ # jupyter-events
+rfc3986-validator==0.1.1 \
+ --hash=sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 \
+ --hash=sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055
+ # via
+ # jsonschema
+ # jupyter-events
+rfc3987-syntax==1.1.0 \
+ --hash=sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f \
+ --hash=sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d
+ # via jsonschema
+rpds-py==0.30.0 \
+ --hash=sha256:0a59119fc6e3f460315fe9d08149f8102aa322299deaa5cab5b40092345c2136 \
+ --hash=sha256:0d08f00679177226c4cb8c5265012eea897c8ca3b93f429e546600c971bcbae7 \
+ --hash=sha256:0ed177ed9bded28f8deb6ab40c183cd1192aa0de40c12f38be4d59cd33cb5c65 \
+ --hash=sha256:1f3587eb9b17f3789ad50824084fa6f81921bbf9a795826570bda82cb3ed91f2 \
+ --hash=sha256:2771c6c15973347f50fece41fc447c054b7ac2ae0502388ce3b6738cd366e3d4 \
+ --hash=sha256:27f4b0e92de5bfbc6f86e43959e6edd1425c33b5e69aab0984a72047f2bcf1e3 \
+ --hash=sha256:39c02563fc592411c2c61d26b6c5fe1e51eaa44a75aa2c8735ca88b0d9599daa \
+ --hash=sha256:3d4a69de7a3e50ffc214ae16d79d8fbb0922972da0356dcf4d0fdca2878559c6 \
+ --hash=sha256:3e62880792319dbeb7eb866547f2e35973289e7d5696c6e295476448f5b63c87 \
+ --hash=sha256:3e8eeb0544f2eb0d2581774be4c3410356eba189529a6b3e36bbbf9696175856 \
+ --hash=sha256:4559c972db3a360808309e06a74628b95eaccbf961c335c8fe0d590cf587456f \
+ --hash=sha256:46e83c697b1f1c72b50e5ee5adb4353eef7406fb3f2043d64c33f20ad1c2fc53 \
+ --hash=sha256:47e77dc9822d3ad616c3d5759ea5631a75e5809d5a28707744ef79d7a1bcfcad \
+ --hash=sha256:47f9a91efc418b54fb8190a6b4aa7813a23fb79c51f4bb84e418f5476c38b8db \
+ --hash=sha256:4c5f36a861bc4b7da6516dbdf302c55313afa09b81931e8280361a4f6c9a2d27 \
+ --hash=sha256:4e7fc54e0900ab35d041b0601431b0a0eb495f0851a0639b6ef90f7741b39a18 \
+ --hash=sha256:51a1234d8febafdfd33a42d97da7a43f5dcb120c1060e352a3fbc0c6d36e2083 \
+ --hash=sha256:5965af57d5848192c13534f90f9dd16464f3c37aaf166cc1da1cae1fd5a34898 \
+ --hash=sha256:5d4c2aa7c50ad4728a094ebd5eb46c452e9cb7edbfdb18f9e1221f597a73e1e7 \
+ --hash=sha256:61046904275472a76c8c90c9ccee9013d70a6d0f73eecefd38c1ae7c39045a08 \
+ --hash=sha256:613aa4771c99f03346e54c3f038e4cc574ac09a3ddfb0e8878487335e96dead6 \
+ --hash=sha256:626a7433c34566535b6e56a1b39a7b17ba961e97ce3b80ec62e6f1312c025551 \
+ --hash=sha256:68f19c879420aa08f61203801423f6cd5ac5f0ac4ac82a2368a9fcd6a9a075e0 \
+ --hash=sha256:692bef75a5525db97318e8cd061542b5a79812d711ea03dbc1f6f8dbb0c5f0d2 \
+ --hash=sha256:6bdfdb946967d816e6adf9a3d8201bfad269c67efe6cefd7093ef959683c8de0 \
+ --hash=sha256:74a3243a411126362712ee1524dfc90c650a503502f135d54d1b352bd01f2404 \
+ --hash=sha256:76fec018282b4ead0364022e3c54b60bf368b9d926877957a8624b58419169b7 \
+ --hash=sha256:7e6ecfcb62edfd632e56983964e6884851786443739dbfe3582947e87274f7cb \
+ --hash=sha256:806f36b1b605e2d6a72716f321f20036b9489d29c51c91f4dd29a3e3afb73b15 \
+ --hash=sha256:9027da1ce107104c50c81383cae773ef5c24d296dd11c99e2629dbd7967a20c6 \
+ --hash=sha256:922e10f31f303c7c920da8981051ff6d8c1a56207dbdf330d9047f6d30b70e5e \
+ --hash=sha256:945dccface01af02675628334f7cf49c2af4c1c904748efc5cf7bbdf0b579f95 \
+ --hash=sha256:95f0802447ac2d10bcc69f6dc28fe95fdf17940367b21d34e34c737870758950 \
+ --hash=sha256:9a4e86e34e9ab6b667c27f3211ca48f73dba7cd3d90f8d5b11be56e5dbc3fb4e \
+ --hash=sha256:9cf69cdda1f5968a30a359aba2f7f9aa648a9ce4b580d6826437f2b291cfc86e \
+ --hash=sha256:a1d0bc22a7cdc173fedebb73ef81e07faef93692b8c1ad3733b67e31e1b6e1b8 \
+ --hash=sha256:a4796a717bf12b9da9d3ad002519a86063dcac8988b030e405704ef7d74d2d9d \
+ --hash=sha256:ac98b175585ecf4c0348fd7b29c3864bda53b805c773cbf7bfdaffc8070c976f \
+ --hash=sha256:acd7eb3f4471577b9b5a41baf02a978e8bdeb08b4b355273994f8b87032000a8 \
+ --hash=sha256:ad1fa8db769b76ea911cb4e10f049d80bf518c104f15b3edb2371cc65375c46f \
+ --hash=sha256:b40fb160a2db369a194cb27943582b38f79fc4887291417685f3ad693c5a1d5d \
+ --hash=sha256:b4dc1a6ff022ff85ecafef7979a2c6eb423430e05f1165d6688234e62ba99a07 \
+ --hash=sha256:ba81a9203d07805435eb06f536d95a266c21e5b2dfbf6517748ca40c98d19e31 \
+ --hash=sha256:c77afbd5f5250bf27bf516c7c4a016813eb2d3e116139aed0096940c5982da94 \
+ --hash=sha256:cdc62c8286ba9bf7f47befdcea13ea0e26bf294bda99758fd90535cbaf408000 \
+ --hash=sha256:d96c2086587c7c30d44f31f42eae4eac89b60dabbac18c7669be3700f13c3ce1 \
+ --hash=sha256:dbd936cde57abfee19ab3213cf9c26be06d60750e60a8e4dd85d1ab12c8b1f40 \
+ --hash=sha256:dc824125c72246d924f7f796b4f63c1e9dc810c7d9e2355864b3c3a73d59ade0 \
+ --hash=sha256:dd8ff7cf90014af0c0f787eea34794ebf6415242ee1d6fa91eaba725cc441e84 \
+ --hash=sha256:e5d3e6b26f2c785d65cc25ef1e5267ccbe1b069c5c21b8cc724efee290554419 \
+ --hash=sha256:e7536cd91353c5273434b4e003cbda89034d67e7710eab8761fd918ec6c69cf8 \
+ --hash=sha256:eb0b93f2e5c2189ee831ee43f156ed34e2a89a78a66b98cadad955972548be5a \
+ --hash=sha256:eb2c4071ab598733724c08221091e8d80e89064cd472819285a9ab0f24bcedb9 \
+ --hash=sha256:ec7c4490c672c1a0389d319b3a9cfcd098dcdc4783991553c332a15acf7249be \
+ --hash=sha256:ee454b2a007d57363c2dfd5b6ca4a5d7e2c518938f8ed3b706e37e5d470801ed \
+ --hash=sha256:f14fc5df50a716f7ece6a80b6c78bb35ea2ca47c499e422aa4463455dd96d56d \
+ --hash=sha256:f251c812357a3fed308d684a5079ddfb9d933860fc6de89f2b7ab00da481e65f \
+ --hash=sha256:f83424d738204d9770830d35290ff3273fbb02b41f919870479fab14b9d303b2 \
+ --hash=sha256:fe5fa731a1fa8a0a56b0977413f8cacac1768dad38d16b3a296712709476fbd5
+ # via
+ # jsonschema
+ # referencing
+send2trash==2.1.0 \
+ --hash=sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c \
+ --hash=sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459
+ # via jupyter-server
+setuptools==80.10.2 \
+ --hash=sha256:8b0e9d10c784bf7d262c4e5ec5d4ec94127ce206e8738f29a437945fbc219b70 \
+ --hash=sha256:95b30ddfb717250edb492926c92b5221f7ef3fbcc2b07579bcd4a27da21d0173
+ # via jupyterlab
+six==1.17.0 \
+ --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
+ --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
+ # via
+ # python-dateutil
+ # rfc3339-validator
+soupsieve==2.8.3 \
+ --hash=sha256:3267f1eeea4251fb42728b6dfb746edc9acaffc4a45b27e19450b676586e8349 \
+ --hash=sha256:ed64f2ba4eebeab06cc4962affce381647455978ffc1e36bb79a545b91f45a95
+ # via beautifulsoup4
+stack-data==0.6.3 \
+ --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \
+ --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695
+ # via ipython
+terminado==0.18.1 \
+ --hash=sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 \
+ --hash=sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e
+ # via
+ # jupyter-server
+ # jupyter-server-terminals
+tinycss2==1.4.0 \
+ --hash=sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7 \
+ --hash=sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289
+ # via bleach
+tornado==6.5.4 \
+ --hash=sha256:053e6e16701eb6cbe641f308f4c1a9541f91b6261991160391bfc342e8a551a1 \
+ --hash=sha256:1768110f2411d5cd281bac0a090f707223ce77fd110424361092859e089b38d1 \
+ --hash=sha256:2d50f63dda1d2cac3ae1fa23d254e16b5e38153758470e9956cbc3d813d40843 \
+ --hash=sha256:50ff0a58b0dc97939d29da29cd624da010e7f804746621c78d14b80238669335 \
+ --hash=sha256:6076d5dda368c9328ff41ab5d9dd3608e695e8225d1cd0fd1e006f05da3635a8 \
+ --hash=sha256:6eb82872335a53dd063a4f10917b3efd28270b56a33db69009606a0312660a6f \
+ --hash=sha256:9c86b1643b33a4cd415f8d0fe53045f913bf07b4a3ef646b735a6a86047dda84 \
+ --hash=sha256:a22fa9047405d03260b483980635f0b041989d8bcc9a313f8fe18b411d84b1d7 \
+ --hash=sha256:d1cf66105dc6acb5af613c054955b8137e34a03698aa53272dbda4afe252be17 \
+ --hash=sha256:d6241c1a16b1c9e4cc28148b1cda97dd1c6cb4fb7068ac1bedc610768dff0ba9 \
+ --hash=sha256:e5fb5e04efa54cf0baabdd10061eb4148e0be137166146fff835745f59ab9f7f \
+ --hash=sha256:fa07d31e0cd85c60713f2b995da613588aa03e1303d75705dca6af8babc18ddc
+ # via
+ # ipykernel
+ # jupyter-client
+ # jupyter-server
+ # jupyterlab
+ # notebook
+ # terminado
+traitlets==5.14.3 \
+ --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \
+ --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f
+ # via
+ # ipykernel
+ # ipython
+ # jupyter-client
+ # jupyter-core
+ # jupyter-events
+ # jupyter-server
+ # jupyterlab
+ # matplotlib-inline
+ # nbclient
+ # nbconvert
+ # nbformat
+typing-extensions==4.15.0 \
+ --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \
+ --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
+ # via beautifulsoup4
+tzdata==2025.3 \
+ --hash=sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1 \
+ --hash=sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7
+ # via arrow
+uri-template==1.3.0 \
+ --hash=sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7 \
+ --hash=sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363
+ # via jsonschema
+urllib3==2.6.3 \
+ --hash=sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed \
+ --hash=sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4
+ # via requests
+wcwidth==0.5.0 \
+ --hash=sha256:1efe1361b83b0ff7877b81ba57c8562c99cf812158b778988ce17ec061095695 \
+ --hash=sha256:f89c103c949a693bf563377b2153082bf58e309919dfb7f27b04d862a0089333
+ # via prompt-toolkit
+webcolors==25.10.0 \
+ --hash=sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d \
+ --hash=sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf
+ # via jsonschema
+webencodings==0.5.1 \
+ --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \
+ --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923
+ # via
+ # bleach
+ # tinycss2
+websocket-client==1.9.0 \
+ --hash=sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98 \
+ --hash=sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef
+ # via jupyter-server
diff --git a/NESP/seabird.ipynb b/NESP/seabird.ipynb
new file mode 100644
index 0000000..289f2b0
--- /dev/null
+++ b/NESP/seabird.ipynb
@@ -0,0 +1,910 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Template to follow per NESP Dataset\n",
+ "\n",
+ "| | | \n",
+ "|---|---| \n",
+ "| Summary (a TL;DR) | | \n",
+ "| Author:| Thomas Galindo |\n",
+ "| Edited:| 2025-02-02 |\n",
+ "| Affiliation:| AODN/IMOS |\n",
+ "| e-mail:| thomas.galindo@utas.edu.au |\n",
+ "| Date of creation:| 2025-02-02 |\n",
+ "| Date of last update:| 2025-02-02 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "This is a template notebook outlining the library stack and content structure for NESP Data Products.\n",
+ "\n",
+ "It outlines:\n",
+ "1. [Connecting to NESP 5.9 datasets](#connecting-to-nesp-59-datasets)\n",
+ "2. [H3 Aggregation and Mapping](#h3-aggregation-and-mapping)\n",
+ "3. [Identifying Trend Sites](#identifying-trend-sites)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## NESP 5.9 Datasets\n",
+ "\n",
+ "| Dataset name | Description | Metadata | S3 URL |\n",
+ "| ------------ | ----------- | -------- | ------ |\n",
+ "| AMSA | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/amsa/) |\n",
+ "| Kelp | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/kelp/kelp.parquet) |\n",
+ "| NRMN | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/nrmn/nrmn.parquet) |\n",
+ "| Seabird | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seabird/seabird.parquet) |\n",
+ "| Seagrass | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seagrass/seagrass.parquet) |\n",
+ "\n",
+ "### S3 Details\n",
+ "NESP 5.9 datasets are currently stored in S3 and are publicly available as per the following table:\n",
+ "\n",
+ "| Bucket | Key | Partitioned |\n",
+ "| -------------------- | -------------------------------------------------------- | ----------- |\n",
+ "| `data-uplift-public` | `stored/datauplift/amsa/year=*/source=*/*.parquet` | [x] |\n",
+ "| `data-uplift-public` | `stored/datauplift/kelp/kelp.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/nrmn/nrmn.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seabird/seabird.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seagrass/seagrass.parquet` | [ ] |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Required Python & Packages\n",
+ "The required packages are found in the [pyproject.toml](pyproject.toml) under `dependencies`.\n",
+ "\n",
+ "### `uv` installation\n",
+ "It is highly recommended to manage python version, environment and projects with [uv](https://github.com/astral-sh/uv).\n",
+ "\n",
+ "```bash\n",
+ "uv venv && source .venv/bin/activate && uv pip install .\n",
+ "```\n",
+ "\n",
+ "### `pip` installation\n",
+ "Otherwise, native python tooling can be used to manage the python environment and project. \n",
+ "\n",
+ "*Note that python3.12 or greater is required.*\n",
+ "\n",
+ "```bash\n",
+ "python -m venv .venv && source .venv/bin/activate && pip install .\n",
+ "```\n",
+ "\n",
+ "### Running Notebooks in Jupyter or IDE\n",
+ "The notebook can be manually run by launching jupyter notebook from the console:\n",
+ "```bash\n",
+ "jupyter notebook\n",
+ "```\n",
+ "Otherwise, if running in an IDE, select the newly created `.venv` as the kernal.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## Notebook Stack\n",
+ "\n",
+ "### IO\n",
+ "`pyarrow` is the defacto parquet and arrow standard library tooling in the python eco-system, offering exceptional performance, documentation and stability.\n",
+ "\n",
+ "Its `fs` class allows connecting to parquet datasets in AWS S3, which is perfect for our use case.\n",
+ "\n",
+ "### Computation\n",
+ "`polars` is fast replacing pandas as the primary python dataframe API. It is more tightly integrated with arrow (and pyarrow), has exceptional performance, greater than memory dataset manipulation, a more consistent and declarative API, built in plotting and many other advantages.\n",
+ "\n",
+ "### Mapping\n",
+ "`pydeck` is a python binding for `deck.gl`, a GPU-powered framework for visual exploratory data analysis of large datasets. It is optimised for Jupyter notebooks and highly customisable.\n",
+ "\n",
+ "Standard mapping tools like `folium` struggle to handle the number of mapped shapes, so we favor the more optimised `pydeck` library.\n",
+ "\n",
+ "### Rich\n",
+ "TODO: Update on `rich` library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set up rich console\n",
+ "import rich\n",
+ "import rich.console\n",
+ "console = rich.console.Console()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "## Connecting to NESP 5.9 Datasets\n",
+ "\n",
+ "NESP 5.9 datasets can be connected to via a pyarrow `S3FileSystem`.\n",
+ "\n",
+ "The `S3FileSystem` manages the connection to the NESP 5.9 datasets stored in S3, resulting in a `pyarrow.Dataset` handle that we may use for efficient loading and subsetting of the dataset.\n",
+ "\n",
+ "Note that a dataset can be comprised of a single parquet file or partitioned (into separate parquet fragments/files).\n",
+ "\n",
+ "This step also makes the dataset schema available and file structure available allowing for pre-filtering without downloading the entire dataset.\n",
+ "\n",
+ "Finally, we may estimate the in memory dataset size by sampling the dataset. If the dataset is small, we may use a `DataFrame` straight away. Otherwise if the dataset is too large, we must pre-aggregate and or filter using a `LazyFrame` before loading the dataset into memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyarrow\n",
+ "import pyarrow.fs\n",
+ "import pyarrow.dataset\n",
+ "import util\n",
+ "\n",
+ "# Construct the anonymous file system responsible for reading from the public S3 bucket\n",
+ "FILE_SYSTEM = pyarrow.fs.S3FileSystem(\n",
+ " region=\"ap-southeast-2\", \n",
+ " anonymous=True,\n",
+ ")\n",
+ "\n",
+ "# Create the dataset connection\n",
+ "# By convention, datasets are labelled `ds`\n",
+ "ds = pyarrow.dataset.dataset(\n",
+ " source=\"data-uplift-public/stored/datauplift/seabird/seabird.parquet\",\n",
+ " filesystem=FILE_SYSTEM,\n",
+ ")\n",
+ "\n",
+ "# The comprising files of the dataset can be inspected\n",
+ "rich.print(list(ds.get_fragments()))\n",
+ "\n",
+ "# The schema of the dataset can be inspected\n",
+ "console.print(\n",
+ " util.generate_schema_rich_table(\n",
+ " schema=ds.schema,\n",
+ " metadata_keys=[\"definition\", \"units\", \"long_name\", \"resolution\"]\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# For the full table scheme use native pyarrow representation\n",
+ "# rich.print(ds.schema)\n",
+ "\n",
+ "# Estimate dataset size\n",
+ "estimated_megabytes = util.estimate_dataset_size(\n",
+ " ds=ds,\n",
+ " n_samples=10_000,\n",
+ ")\n",
+ "rich.print(f\"DS estimated size: {round(estimated_megabytes, 2)}MB\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "## Loading Data\n",
+ "The dataset handle is our connection to data in S3. We can use this handle to load data directly into a `DataFrame` or `LazyFrame`\n",
+ "\n",
+ "### DataFrame\n",
+ "For smaller datasets the `DataFrame` is preferrable. This loads the entire dataset into memory ready for analysis.\n",
+ "\n",
+ "### LazyFrame\n",
+ "For larger datasets the `DataFrame` may not fit into memory. In these cases the `LazyFrame` is ideal as it allows the dataset to be filtered and or aggregated prior to loading the dataset, allowing it to fit into memory, ready for analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import polars\n",
+ "import polars_h3\n",
+ "\n",
+ "# Option (a): Load entire dataset in memory as a dataframe\n",
+ "# Suitable for smaller datasets that fit into memory\n",
+ "df = polars.DataFrame(\n",
+ " data=ds.to_table(),\n",
+ ")\n",
+ "\n",
+ "# Option (b): Scan entire dataset as a lazyframe\n",
+ "# Suitable for larger datasets that don't fit into memory\n",
+ "lf = polars.scan_pyarrow_dataset(\n",
+ " source=ds,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "## H3 Aggregation and Mapping\n",
+ "The NESP 5.9 datasets are aggregated data products that cover large regions and time periods.\n",
+ "\n",
+ "It is useful to first aggregate the datasets so that we may understand their temporal-spatial bounds.\n",
+ "\n",
+ "### H3 Indexing\n",
+ "The `h3` library allows us to spatially index datasets at different [resolutions](https://h3geo.org/docs/core-library/restable#average-area-in-km2) (zoom levels) according to the record latitude and longitude.\n",
+ "\n",
+ "Additionally, the `polars_h3` library offers a polars style interface for faster tagging with `polars.DataFrames`:\n",
+ "```python\n",
+ "\n",
+ "# Add a h3 index to any dataframe with latitude and longitude columns\n",
+ "df = df.with_columns(\n",
+ "\n",
+ " # Polars Expression Shorthand to access the `polars_h3` namespace\n",
+ " polars_h3.latlng_to_cell(\n",
+ "\n",
+ " # Specify the latitude and longitude columns\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ "\n",
+ " # Specify the resolution\n",
+ " resolution=5,\n",
+ "\n",
+ " # Specify the index return type, can be `polars.Int64` or `polars.String`\n",
+ " return_dtype=polars.String,\n",
+ " )\n",
+ " \n",
+ " # Alias to `h3Index`\n",
+ " .alias(\"h3Index\"),\n",
+ ")\n",
+ "```\n",
+ "\n",
+ "### H3 Mapping\n",
+ "The `pydeck` library offers a built in [`H3HexagonLayer`](https://deck.gl/docs/api-reference/geo-layers/h3-hexagon-layer) layer type that greatly reduces complexity in mapping H3 Shapes:\n",
+ "\n",
+ "```python\n",
+ "\n",
+ "# Generate a h3 hexagon layer displayable on a pydeck map\n",
+ "pydeck.Layer(\n",
+ "\n",
+ " # Specify this is a H3 Hexagon Layer\n",
+ " \"H3HexagonLayer\",\n",
+ "\n",
+ " # Unfortunately no polars dataframe support, so cast to `pandas.DataFrame`\n",
+ " df.to_pandas(use_pyarrow_extension_array=True),\n",
+ "\n",
+ " # Specify the hexagon\n",
+ " get_hexagon=\"h3Index\",\n",
+ "\n",
+ ")\n",
+ "```\n",
+ "This is rendered with a [`pydeck.Deck`](https://deck.gl/docs/api-reference/core/deck):\n",
+ "```python\n",
+ "\n",
+ "# Generate a deck with h3 layers\n",
+ "deck = pydeck.Deck(\n",
+ "\n",
+ " # Supply the h3 layers\n",
+ " layers=h3_layers,\n",
+ ")\n",
+ "deck.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(\n",
+ "\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydeck\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ "\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.GLOBAL_VIEW_STATE,\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {},
+ "source": [
+ "## Australian Marine Regions\n",
+ "Each occurrence has been tagged by the following Australian Marine Regions (see table below). Note that the taggin is not 100% accurate at the region boundaries; it is accurate to ~6km.\n",
+ "\n",
+ "Note that the Great Barrier Reef has been included from the World Heritage Areas map.\n",
+ "\n",
+ "| Marine Region Type | Map Explore URL | Metadata URL (REST/About) | GeoJSON API Endpoint |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| **Commonwealth Marine Regions** | [Explore](https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **World Heritage Areas** | [Explore](https://fed.dcceew.gov.au/datasets/erin::australia-world-heritage-areas/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **Marine Parks** | [Explore](https://fed.dcceew.gov.au/datasets/2b3eb1d42b8d4319900cf4777f0a83b9_0/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Provincial** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Provincial/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Mesoscale** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Mesoscale/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "\n",
+ "Follow the **Explore** links to explore the Australian Marine Regions. The Commonwealth Marine Regions explore map has been embedded below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import IPython.display\n",
+ "\n",
+ "# Display the Commonwealth Marine Regions\n",
+ "IPython.display.IFrame(\n",
+ " src=\"https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore?location=-33.665700%2C140.517300%2C4\",\n",
+ " width=\"100%\", \n",
+ " height=\"1000px\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15",
+ "metadata": {},
+ "source": [
+ "### Exploring Available Marine Regions Tags\n",
+ "\n",
+ "Australian Marine Regions Tags have the following syntax:\n",
+ "\n",
+ "| TAG | TYPE | OPTIONAL SUPER REGIONS | REGION |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| `Commonwealth Marine Region: South-east` | `Commonwealth Marine Region` | | `South-east` |\n",
+ "| `Marine Park:National Park Zone:Freycinet` | `Marine Park` | `National Park Zone` | `Freycinet` |\n",
+ "| `IMCRA:Provincial:Water Type:Cold Temperate Waters` | `IMCRA` | `Provincial`, `Water Type` | `Cold Temperate Waters` |\n",
+ "\n",
+ "Extracting the available Australian Marine Regions Tags requires spliting the delimited column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract the unique tags\n",
+ "australian_marine_regions_tags = df[\"australianMarineRegionsTags\"].str.split(\"|\").explode().value_counts(sort=True).drop_nulls()\n",
+ "\n",
+ "# Maniuplate polars display config to display full tag names\n",
+ "with polars.Config(\n",
+ " fmt_str_lengths=128,\n",
+ " tbl_rows=20\n",
+ "):\n",
+ " display(australian_marine_regions_tags)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "### Filtering to All Data within Australian Marine Regions\n",
+ "We may filter to a particular requires a using not `is_null` constraint:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the commonwealth marine regions\n",
+ "australian_marine_regions_df = df.filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").is_null().not_()\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " australian_marine_regions_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=5,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=australian_marine_regions_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "### Filtering to a particular Marine Region\n",
+ "We may filter to a particular requires a using `str.contains` expression:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the commonwealth marine regions\n",
+ "australian_marine_regions_df = df.filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").str.contains(\n",
+ " pattern=\"Commonwealth Marine Region:South-east\",\n",
+ " literal=True,\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " australian_marine_regions_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=5,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=australian_marine_regions_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21",
+ "metadata": {},
+ "source": [
+ "## Identifying Trend Sites\n",
+ "In the global map we can see there are some sites with a high density of records. These are the most ideal sites for trend analysis.\n",
+ "\n",
+ "### Per Region\n",
+ "[Regions](#australian-marine-regions) with a high density of records are typically good candidates for trend analysis.\n",
+ "\n",
+ "### Per Dataset\n",
+ "Additionally, the singular datasets that comprise the aggregated data product are typically good candidates for trend analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22",
+ "metadata": {},
+ "source": [
+ "### Observation Effort vs Organism Quantity\n",
+ "Observation effort is the number of occurrence records while Organism Quantity is the recorded count for a specific species.\n",
+ "\n",
+ "Observation effort must be taken into account when analysing trends; fluctuations in observation effort will distort the reported occurrences."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "yearly_observation_intensity = (\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"nRecord\"),\n",
+ " )\n",
+ " .plot.bar(\n",
+ " x=\"year\",\n",
+ " y=\"nRecord\",\n",
+ " color=\"datasetName\",\n",
+ " )\n",
+ ")\n",
+ "yearly_quantity_intensity = (\n",
+ " df\n",
+ " # Some organismQuantity values are \"present\", which must be filtered out\n",
+ " .filter(\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64, strict=False).is_null().not_(),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"organismQuantity\").sum(),\n",
+ " )\n",
+ " .plot.bar(\n",
+ " x=\"year\",\n",
+ " y=\"organismQuantity\",\n",
+ " color=\"datasetName\",\n",
+ " )\n",
+ ")\n",
+ "yearly_observation_intensity & yearly_quantity_intensity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24",
+ "metadata": {},
+ "source": [
+ "### Mapping the Spatial Extent of a Particular Dataset\n",
+ "Similarly to mapping Australian Marine Regions, we may map particular datasets by applying a string constraint.\n",
+ "\n",
+ "#### Finding Available Datasets\n",
+ "We may find available datasets by aggregating by the `datasetName` column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[\"datasetName\"].value_counts(sort=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the `seabird atlas sea` dataset\n",
+ "seabird_atlas_sea_df = df.filter(\n",
+ " polars.col(\"datasetName\").eq(\"seabird_atlas_southeast_australia\"),\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " seabird_atlas_sea_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=seabird_atlas_sea_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27",
+ "metadata": {},
+ "source": [
+ "### Identifying the Most Occurring Species"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "top_ten_species_df = seabird_atlas_sea_df[\"scientificName\"].value_counts(sort=True).head(10)\n",
+ "top_ten_species_list = top_ten_species_df[\"scientificName\"].to_list()\n",
+ "display(top_ten_species_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29",
+ "metadata": {},
+ "source": [
+ "### Plotting the Species Occurrences Over Time (Yearly)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Observe the large fluctuations in observation effort in even a single dataset\n",
+ "# This requires normalisation in future graphing\n",
+ "(\n",
+ " seabird_atlas_sea_df\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"observation_effort\"),\n",
+ " )\n",
+ " .plot.line(\n",
+ " x=\"year\",\n",
+ " y=\"observation_effort\",\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Observe some species quantity (rarity/observation focus) varies wildy as well\n",
+ "# This requires normalisation in future graphing\n",
+ "(\n",
+ " seabird_atlas_sea_df\n",
+ " .filter(\n",
+ " polars.col(\"scientificName\").is_in(\n",
+ " seabird_atlas_sea_df[\"scientificName\"].value_counts(sort=True)[\"scientificName\"][:21].to_list(),\n",
+ " ),\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64, strict=False).is_not_null(),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"scientificName\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " ).agg(\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64).sum(),\n",
+ " ).sort(\n",
+ " polars.col(\"organismQuantity\"),\n",
+ " descending=True,\n",
+ " ).plot.bar(\n",
+ " x=\"organismQuantity\",\n",
+ " y=\"scientificName\",\n",
+ " color=\"year\",\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32",
+ "metadata": {},
+ "source": [
+ "### Observation Effort and Species Normalised Chart\n",
+ "\n",
+ "This chart tracks species population trends by stripping away biases in data collection. It shifts the focus from \"how many were seen\" to \"how common they were relative to our effort.\"\n",
+ "\n",
+ "#### Normalise Observation Effort\n",
+ "To account for varying survey frequency each year, we calculate **Abundance per Unit Effort**.\n",
+ "* **Logic:** `annual_sum / total_annual_effort`\n",
+ "* **Purpose:** Prevents years with high survey volume from appearing falsely \"better\" than years with fewer surveys.\n",
+ "\n",
+ "#### Normalise Species Focus/Rarity\n",
+ "To compare common and rare species on the same scale, we calculate a **Relative Abundance Index**.\n",
+ "* **Logic:** `abundance_per_effort / species_historical_mean`\n",
+ "* **Purpose:** Centers every species' baseline at **1.0**. This allows us to visualize fluctuations as a percentage of their own \"normal\" rather than comparing raw counts between different species.\n",
+ "\n",
+ "#### How To Read\n",
+ "* **> 1.0:** The species was more abundant than average that year.\n",
+ "* **< 1.0:** The species was less abundant than average that year.\n",
+ "* **Independent Y-Axes:** Each species is scaled to its own range to maximize clarity of its specific trend."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Produce a combined effort and species-normalised chart\n",
+ "year_chart_combined = (\n",
+ " seabird_atlas_sea_df\n",
+ " .filter(\n",
+ " polars.col(\"scientificName\").is_in(\n",
+ " seabird_atlas_sea_df[\"scientificName\"].value_counts(sort=True)[\"scientificName\"][:21].to_list(),\n",
+ " ),\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64, strict=False).is_null().not_(),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " polars.col(\"scientificName\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"organismQuantity\").cast(polars.Int64).sum().alias(\"annual_sum\"),\n",
+ " polars.len().alias(\"species_records\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # 1. Effort Normalisation: Total records (effort) across ALL species for that year\n",
+ " polars.col(\"species_records\").sum().over(\"year\").alias(\"total_annual_effort\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # Abundance per unit of effort\n",
+ " (polars.col(\"annual_sum\") / polars.col(\"total_annual_effort\")).alias(\"abundance_per_effort\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # 2. Species Normalisation: Mean abundance_per_effort for this species across all years\n",
+ " polars.col(\"abundance_per_effort\").mean().over(\"scientificName\").alias(\"species_historical_effort_mean\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # Final Index: How this year's effort-adjusted abundance compares to the species average\n",
+ " (polars.col(\"abundance_per_effort\") / polars.col(\"species_historical_effort_mean\")).clip(0).alias(\"relative_abundance_index\"),\n",
+ " )\n",
+ " .plot.line(\n",
+ " x=\"year\",\n",
+ " y=\"relative_abundance_index\",\n",
+ " color=\"scientificName\",\n",
+ " )\n",
+ " .facet(\n",
+ " facet=\"scientificName\",\n",
+ " columns=3,\n",
+ " )\n",
+ " .resolve_scale(\n",
+ " y=\"independent\",\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "year_chart_combined"
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "formats": "ipynb,py:percent"
+ },
+ "kernelspec": {
+ "display_name": "NESP",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NESP/seagrass.ipynb b/NESP/seagrass.ipynb
new file mode 100644
index 0000000..354add9
--- /dev/null
+++ b/NESP/seagrass.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Seagrass Demo Notebook\n",
+ "\n",
+ "| | | \n",
+ "|---|---| \n",
+ "| Summary (a TL;DR) | | \n",
+ "| Author:| Thomas Galindo |\n",
+ "| Edited:| 2025-02-02 |\n",
+ "| Affiliation:| AODN/IMOS |\n",
+ "| e-mail:| thomas.galindo@utas.edu.au |\n",
+ "| Date of creation:| 2025-02-02 |\n",
+ "| Date of last update:| 2025-02-02 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "This is a template notebook outlining the library stack and content structure for NESP Data Products.\n",
+ "\n",
+ "It outlines:\n",
+ "1. [Connecting to NESP 5.9 datasets](#connecting-to-nesp-59-datasets)\n",
+ "2. [H3 Aggregation and Mapping](#h3-aggregation-and-mapping)\n",
+ "3. [Identifying Trend Sites](#identifying-trend-sites)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## NESP 5.9 Datasets\n",
+ "\n",
+ "| Dataset name | Description | Metadata | S3 URL |\n",
+ "| ------------ | ----------- | -------- | ------ |\n",
+ "| AMSA | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/amsa/) |\n",
+ "| Kelp | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/kelp/kelp.parquet) |\n",
+ "| NRMN | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/nrmn/nrmn.parquet) |\n",
+ "| Seabird | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seabird/seabird.parquet) |\n",
+ "| Seagrass | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seagrass/seagrass.parquet) |\n",
+ "\n",
+ "### S3 Details\n",
+ "NESP 5.9 datasets are currently stored in S3 and are publicly available as per the following table:\n",
+ "\n",
+ "| Bucket | Key | Partitioned |\n",
+ "| -------------------- | -------------------------------------------------------- | ----------- |\n",
+ "| `data-uplift-public` | `stored/datauplift/amsa/year=*/source=*/*.parquet` | [x] |\n",
+ "| `data-uplift-public` | `stored/datauplift/kelp/kelp.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/nrmn/nrmn.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seabird/seabird.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seagrass/seagrass.parquet` | [ ] |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Required Python & Packages\n",
+ "The required packages are found in the [pyproject.toml](pyproject.toml) under `dependencies`.\n",
+ "\n",
+ "### `uv` installation\n",
+ "It is highly recommended to manage python version, environment and projects with [uv](https://github.com/astral-sh/uv).\n",
+ "\n",
+ "```bash\n",
+ "uv venv && source .venv/bin/activate && uv pip install .\n",
+ "```\n",
+ "\n",
+ "### `pip` installation\n",
+ "Otherwise, native python tooling can be used to manage the python environment and project. \n",
+ "\n",
+ "*Note that python3.12 or greater is required.*\n",
+ "\n",
+ "```bash\n",
+ "python -m venv .venv && source .venv/bin/activate && pip install .\n",
+ "```\n",
+ "\n",
+ "### Running Notebooks in Jupyter or IDE\n",
+ "The notebook can be manually run by launching jupyter notebook from the console:\n",
+ "```bash\n",
+ "jupyter notebook\n",
+ "```\n",
+ "Otherwise, if running in an IDE, select the newly created `.venv` as the kernal.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## Notebook Stack\n",
+ "\n",
+ "### IO\n",
+ "`pyarrow` is the defacto parquet and arrow standard library tooling in the python eco-system, offering exceptional performance, documentation and stability.\n",
+ "\n",
+ "Its `fs` class allows connecting to parquet datasets in AWS S3, which is perfect for our use case.\n",
+ "\n",
+ "### Computation\n",
+ "`polars` is fast replacing pandas as the primary python dataframe API. It is more tightly integrated with arrow (and pyarrow), has exceptional performance, greater than memory dataset manipulation, a more consistent and declarative API, built in plotting and many other advantages.\n",
+ "\n",
+ "### Mapping\n",
+ "`pydeck` is a python binding for `deck.gl`, a GPU-powered framework for visual exploratory data analysis of large datasets. It is optimised for Jupyter notebooks and highly customisable.\n",
+ "\n",
+ "Standard mapping tools like `folium` struggle to handle the number of mapped shapes, so we favor the more optimised `pydeck` library.\n",
+ "\n",
+ "### Rich\n",
+ "TODO: Update on `rich` library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set up rich console\n",
+ "import rich\n",
+ "import rich.console\n",
+ "console = rich.console.Console()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "## Connecting to NESP 5.9 Datasets\n",
+ "\n",
+ "NESP 5.9 datasets can be connected to via a pyarrow `S3FileSystem`.\n",
+ "\n",
+ "The `S3FileSystem` manages the connection to the NESP 5.9 datasets stored in S3, resulting in a `pyarrow.Dataset` handle that we may use for efficient loading and subsetting of the dataset.\n",
+ "\n",
+ "Note that a dataset can be comprised of a single parquet file or partitioned (into separate parquet fragments/files).\n",
+ "\n",
+ "This step also makes the dataset schema available and file structure available allowing for pre-filtering without downloading the entire dataset.\n",
+ "\n",
+ "Finally, we may estimate the in memory dataset size by sampling the dataset. If the dataset is small, we may use a `DataFrame` straight away. Otherwise if the dataset is too large, we must pre-aggregate and or filter using a `LazyFrame` before loading the dataset into memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyarrow\n",
+ "import pyarrow.fs\n",
+ "import pyarrow.dataset\n",
+ "import util\n",
+ "\n",
+ "# Construct the anonymous file system responsible for reading from the public S3 bucket\n",
+ "FILE_SYSTEM = pyarrow.fs.S3FileSystem(\n",
+ " region=\"ap-southeast-2\", \n",
+ " anonymous=True,\n",
+ ")\n",
+ "\n",
+ "# Create the dataset connection\n",
+ "# By convention, datasets are labelled `ds`\n",
+ "ds = pyarrow.dataset.dataset(\n",
+ " source=\"data-uplift-public/stored/datauplift/seagrass/seagrass.parquet\",\n",
+ " filesystem=FILE_SYSTEM,\n",
+ ")\n",
+ "\n",
+ "# The comprising files of the dataset can be inspected\n",
+ "rich.print(list(ds.get_fragments()))\n",
+ "\n",
+ "# The schema of the dataset can be inspected\n",
+ "console.print(\n",
+ " util.generate_schema_rich_table(\n",
+ " schema=ds.schema,\n",
+ " metadata_keys=[\"definition\", \"units\", \"long_name\", \"resolution\"]\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# For the full table scheme use native pyarrow representation\n",
+ "rich.print(ds.schema)\n",
+ "\n",
+ "# Estimate dataset size\n",
+ "estimated_megabytes = util.estimate_dataset_size(\n",
+ " ds=ds,\n",
+ " n_samples=10_000,\n",
+ ")\n",
+ "rich.print(f\"DS estimated size: {round(estimated_megabytes, 2)}MB\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "## Loading Data\n",
+ "The dataset handle is our connection to data in S3. We can use this handle to load data directly into a `DataFrame` or `LazyFrame`\n",
+ "\n",
+ "### DataFrame\n",
+ "For smaller datasets the `DataFrame` is preferrable. This loads the entire dataset into memory ready for analysis.\n",
+ "\n",
+ "### LazyFrame\n",
+ "For larger datasets the `DataFrame` may not fit into memory. In these cases the `LazyFrame` is ideal as it allows the dataset to be filtered and or aggregated prior to loading the dataset, allowing it to fit into memory, ready for analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import polars\n",
+ "import polars_h3\n",
+ "\n",
+ "# Option (a): Load entire dataset in memory as a dataframe\n",
+ "# Suitable for smaller datasets that fit into memory\n",
+ "df = polars.DataFrame(\n",
+ " data=ds.to_table(),\n",
+ ")\n",
+ "\n",
+ "# Option (b): Scan entire dataset as a lazyframe\n",
+ "# Suitable for larger datasets that don't fit into memory\n",
+ "lf = polars.scan_pyarrow_dataset(\n",
+ " source=ds,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "## H3 Aggregation and Mapping\n",
+ "The NESP 5.9 datasets are aggregated data products that cover large regions and time periods.\n",
+ "\n",
+ "It is useful to first aggregate the datasets so that we may understand their temporal-spatial bounds.\n",
+ "\n",
+ "### H3 Indexing\n",
+ "The `h3` library allows us to spatially index datasets at different [resolutions](https://h3geo.org/docs/core-library/restable#average-area-in-km2) (zoom levels) according to the record latitude and longitude.\n",
+ "\n",
+ "Additionally, the `polars_h3` library offers a polars style interface for faster tagging with `polars.DataFrames`:\n",
+ "```python\n",
+ "\n",
+ "# Add a h3 index to any dataframe with latitude and longitude columns\n",
+ "df = df.with_columns(\n",
+ "\n",
+ " # Polars Expression Shorthand to access the `polars_h3` namespace\n",
+ " polars_h3.latlng_to_cell(\n",
+ "\n",
+ " # Specify the latitude and longitude columns\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ "\n",
+ " # Specify the resolution\n",
+ " resolution=5,\n",
+ "\n",
+ " # Specify the index return type, can be `polars.Int64` or `polars.String`\n",
+ " return_dtype=polars.String,\n",
+ " )\n",
+ " \n",
+ " # Alias to `h3Index`\n",
+ " .alias(\"h3Index\"),\n",
+ ")\n",
+ "```\n",
+ "\n",
+ "### H3 Mapping\n",
+ "The `pydeck` library offers a built in [`H3HexagonLayer`](https://deck.gl/docs/api-reference/geo-layers/h3-hexagon-layer) layer type that greatly reduces complexity in mapping H3 Shapes:\n",
+ "\n",
+ "```python\n",
+ "\n",
+ "# Generate a h3 hexagon layer displayable on a pydeck map\n",
+ "pydeck.Layer(\n",
+ "\n",
+ " # Specify this is a H3 Hexagon Layer\n",
+ " \"H3HexagonLayer\",\n",
+ "\n",
+ " # Unfortunately no polars dataframe support, so cast to `pandas.DataFrame`\n",
+ " df.to_pandas(use_pyarrow_extension_array=True),\n",
+ "\n",
+ " # Specify the hexagon\n",
+ " get_hexagon=\"h3Index\",\n",
+ "\n",
+ ")\n",
+ "```\n",
+ "This is rendered with a [`pydeck.Deck`](https://deck.gl/docs/api-reference/core/deck):\n",
+ "```python\n",
+ "\n",
+ "# Generate a deck with h3 layers\n",
+ "deck = pydeck.Deck(\n",
+ "\n",
+ " # Supply the h3 layers\n",
+ " layers=h3_layers,\n",
+ ")\n",
+ "deck.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydeck\n",
+ "import util\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ "\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=df),\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "## Australian Marine Regions\n",
+ "Each occurrence has been tagged by the following Australian Marine Regions (see table below). Note that the taggin is not 100% accurate at the region boundaries; it is accurate to ~6km.\n",
+ "\n",
+ "Note that the Great Barrier Reef has been included from the World Heritage Areas map.\n",
+ "\n",
+ "| Marine Region Type | Map Explore URL | Metadata URL (REST/About) | GeoJSON API Endpoint |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| **Commonwealth Marine Regions** | [Explore](https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **World Heritage Areas** | [Explore](https://fed.dcceew.gov.au/datasets/erin::australia-world-heritage-areas/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **Marine Parks** | [Explore](https://fed.dcceew.gov.au/datasets/2b3eb1d42b8d4319900cf4777f0a83b9_0/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Provincial** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Provincial/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Mesoscale** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Mesoscale/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "\n",
+ "Follow the **Explore** links to explore the Australian Marine Regions. The Commonwealth Marine Regions explore map has been embedded below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import IPython.display\n",
+ "\n",
+ "# Display the Commonwealth Marine Regions\n",
+ "IPython.display.IFrame(\n",
+ " src=\"https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore?location=-33.665700%2C140.517300%2C4\",\n",
+ " width=\"100%\", \n",
+ " height=\"1000px\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14",
+ "metadata": {},
+ "source": [
+ "### Exploring Available Marine Regions Tags\n",
+ "\n",
+ "Australian Marine Regions Tags have the following syntax:\n",
+ "\n",
+ "| TAG | TYPE | OPTIONAL SUPER REGIONS | REGION |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| `Commonwealth Marine Region: South-east` | `Commonwealth Marine Region` | | `South-east` |\n",
+ "| `Marine Park:National Park Zone:Freycinet` | `Marine Park` | `National Park Zone` | `Freycinet` |\n",
+ "| `IMCRA:Provincial:Water Type:Cold Temperate Waters` | `IMCRA` | `Provincial`, `Water Type` | `Cold Temperate Waters` |\n",
+ "\n",
+ "Extracting the available Australian Marine Regions Tags requires spliting the delimited column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract the unique tags\n",
+ "australian_marine_regions_tags = df[\"australianMarineRegionsTags\"].str.split(\"|\").explode().value_counts(sort=True).drop_nulls()\n",
+ "\n",
+ "# Maniuplate polars display config to display full tag names\n",
+ "with polars.Config(\n",
+ " fmt_str_lengths=128,\n",
+ " tbl_rows=20\n",
+ "):\n",
+ " display(australian_marine_regions_tags)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the commonwealth marine regions\n",
+ "great_barrier_reef_df = df.filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").str.contains(\n",
+ " pattern=\"World Heritage Area:Great Barrier Reef\",\n",
+ " literal=True,\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " great_barrier_reef_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"nRecords\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"nRecords\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=australian_marine_regions_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "## Identifying Trend Sites\n",
+ "In the global map we can see there are some sites with a high density of records. These are the most ideal sites for trend analysis.\n",
+ "\n",
+ "### Per Region\n",
+ "[Regions](#australian-marine-regions) with a high density of records are typically good candidates for trend analysis.\n",
+ "\n",
+ "### Per Dataset\n",
+ "Additionally, the singular datasets that comprise the aggregated data product are typically good candidates for trend analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18",
+ "metadata": {},
+ "source": [
+ "### Mapping the Spatial Extent of a Particular Dataset\n",
+ "Similarly to mapping Australian Marine Regions, we may map particular datasets by applying a string constraint.\n",
+ "\n",
+ "#### Finding Available Datasets\n",
+ "We may find available datasets by aggregating by the `datasetName` column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with polars.Config(\n",
+ " fmt_str_lengths=256,\n",
+ "):\n",
+ " display(df[\"datasetName\"].value_counts(sort=True))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the `NESP MaC Project 1.13` dataset\n",
+ "nesp_mac_df = df.filter(\n",
+ " polars.col(\"datasetName\").eq(\"Four Decades of Seagrass Spatial Data from Torres Strait and Gulf of Carpentaria (NESP MaC Project 1.13, TropWATER JCU)\"),\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " nesp_mac_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"nRecords\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"nRecords\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=seabird_atlas_sea_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21",
+ "metadata": {},
+ "source": [
+ "### Identifying the Most Occurring Species"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "most_occurring_species_df = nesp_mac_df.filter(\n",
+ " polars.col(\"occurrenceStatus\").eq(\"present\"),\n",
+ ")[\"scientificName\"].value_counts(sort=True)\n",
+ "most_occurring_species_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23",
+ "metadata": {},
+ "source": [
+ "### Plotting the Species Occurrences Over Time (Yearly)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Observe the large fluctuations in observation effort in even a single dataset\n",
+ "# This requires normalisation in future graphing\n",
+ "(\n",
+ " nesp_mac_df\n",
+ " .group_by(\n",
+ " polars.col(\"startYear\").alias(\"year\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"observation_effort\"),\n",
+ " )\n",
+ " .plot.line(\n",
+ " x=\"year\",\n",
+ " y=\"observation_effort\",\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Observe some species quantity (rarity/observation focus) varies wildy as well\n",
+ "# This requires normalisation in future graphing\n",
+ "(\n",
+ " nesp_mac_df\n",
+ " .filter(\n",
+ " polars.col(\"scientificName\").is_in(\n",
+ " nesp_mac_df[\"scientificName\"].value_counts(sort=True)[\"scientificName\"][:21].to_list(),\n",
+ " ),\n",
+ " polars.col(\"occurrenceStatus\").eq(\"present\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"scientificName\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " polars.col(\"startYear\").alias(\"year\"),\n",
+ " ).agg(\n",
+ " polars.len().alias(\"observation\"),\n",
+ " ).sort(\n",
+ " polars.col(\"observation\"),\n",
+ " descending=True,\n",
+ " ).plot.bar(\n",
+ " x=\"observation\",\n",
+ " y=\"scientificName\",\n",
+ " color=\"year\",\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26",
+ "metadata": {},
+ "source": [
+ "### Observation Effort and Species Normalised Chart\n",
+ "\n",
+ "This chart tracks species population trends by stripping away biases in data collection. It shifts the focus from \"how many were seen\" to \"how common they were relative to our effort.\"\n",
+ "\n",
+ "#### Normalise Observation Effort\n",
+ "To account for varying survey frequency each year, we calculate **Abundance per Unit Effort**.\n",
+ "* **Logic:** `annual_sum / total_annual_effort`\n",
+ "* **Purpose:** Prevents years with high survey volume from appearing falsely \"better\" than years with fewer surveys.\n",
+ "\n",
+ "#### Normalise Species Focus/Rarity\n",
+ "To compare common and rare species on the same scale, we calculate a **Relative Abundance Index**.\n",
+ "* **Logic:** `abundance_per_effort / species_historical_mean`\n",
+ "* **Purpose:** Centers every species' baseline at **1.0**. This allows us to visualize fluctuations as a percentage of their own \"normal\" rather than comparing raw counts between different species.\n",
+ "\n",
+ "#### How To Read\n",
+ "* **> 1.0:** The species was more abundant than average that year.\n",
+ "* **< 1.0:** The species was less abundant than average that year.\n",
+ "* **Independent Y-Axes:** Each species is scaled to its own range to maximize clarity of its specific trend."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Produce a combined effort and species-normalised chart for Presence/Absence\n",
+ "year_chart_presence_absence = (\n",
+ " nesp_mac_df\n",
+ " .filter(\n",
+ " polars.col(\"scientificName\").is_in(\n",
+ " most_occurring_species_df[\"scientificName\"][:9].to_list(),\n",
+ " ),\n",
+ " polars.col(\"occurrenceStatus\").eq(\"present\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"startYear\").alias(\"year\"),\n",
+ " polars.col(\"scientificName\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .agg(\n",
+ " # Instead of summing quantities, we count detections (Presence)\n",
+ " polars.len().alias(\"species_detections\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # 1. Effort: Total records (all surveys/observations) for that year\n",
+ " polars.col(\"species_detections\").sum().over(\"year\").alias(\"total_annual_effort\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # Frequency of Occurrence: How often was it seen per unit of effort?\n",
+ " (polars.col(\"species_detections\") / polars.col(\"total_annual_effort\")).alias(\"frequency_of_occurrence\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # 2. Species Normalisation: Mean frequency for this species across all years\n",
+ " polars.col(\"frequency_of_occurrence\").mean().over(\"scientificName\").alias(\"species_historical_freq_mean\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " # Final Index: Relative Presence Index\n",
+ " (polars.col(\"frequency_of_occurrence\") / polars.col(\"species_historical_freq_mean\")).clip(0).alias(\"relative_presence_index\"),\n",
+ " )\n",
+ " .plot.line(\n",
+ " x=\"year\",\n",
+ " y=\"relative_presence_index\",\n",
+ " color=\"scientificName\",\n",
+ " )\n",
+ " .facet(\n",
+ " facet=\"scientificName\",\n",
+ " columns=3,\n",
+ " )\n",
+ " .resolve_scale(\n",
+ " y=\"independent\",\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "year_chart_presence_absence"
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "formats": "ipynb,py:percent"
+ },
+ "kernelspec": {
+ "display_name": "NESP",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NESP/temp.ipynb b/NESP/temp.ipynb
new file mode 100644
index 0000000..c0074f7
--- /dev/null
+++ b/NESP/temp.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pathlib\n",
+ "import util\n",
+ "import polars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gdf = util.generate_geometry_table_from_shapefile(pathlib.Path(\"OffshoreRenewable_Energy_Infrastructure_Regions.zip\"))\n",
+ "with polars.Config(\n",
+ " tbl_rows=16\n",
+ "):\n",
+ " display(gdf[\"Name\"].unique().sort())\n",
+ "\n",
+ "gdf = (\n",
+ " gdf\n",
+ " .filter(\n",
+ " polars.col(\"Status\").eq(\"Declared\"),\n",
+ " )\n",
+ " .select(\n",
+ " polars.concat_str(\n",
+ " polars.col(\"Region\"),\n",
+ " separator=\":\",\n",
+ " ).alias(\"tag\"),\n",
+ " polars.col(\"geometry\"),\n",
+ " )\n",
+ ")\n",
+ "gdf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h3_indexed_df = util.generate_wkt_to_h3_index(\n",
+ " gdf=gdf,\n",
+ " h3_resolution=7,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydeck\n",
+ "import polars_h3\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " h3_indexed_df\n",
+ " ),\n",
+ " hexagon_index_column_name=\"h3_index\",\n",
+ " aggregate_column_name=\"nRecords\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " # tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(\n",
+ " df=h3_indexed_df.with_columns(\n",
+ " polars_h3.cell_to_lat(polars.col(\"h3_index\")).alias(\"decimalLatitude\"),\n",
+ " polars_h3.cell_to_lng(polars.col(\"h3_index\")).alias(\"decimalLongitude\"),\n",
+ " ),\n",
+ " ),\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "NESP",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NESP/template.ipynb b/NESP/template.ipynb
new file mode 100644
index 0000000..1f43ee4
--- /dev/null
+++ b/NESP/template.ipynb
@@ -0,0 +1,1284 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Template to follow per NESP Dataset\n",
+ "\n",
+ "| | | \n",
+ "|---|---| \n",
+ "| Summary (a TL;DR) | | \n",
+ "| Author:| Thomas Galindo |\n",
+ "| Edited:| 2025-02-02 |\n",
+ "| Affiliation:| AODN/IMOS |\n",
+ "| e-mail:| thomas.galindo@utas.edu.au |\n",
+ "| Date of creation:| 2025-02-02 |\n",
+ "| Date of last update:| 2025-02-02 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "This is a template notebook outlining the library stack and content structure for NESP Data Products.\n",
+ "\n",
+ "It outlines:\n",
+ "1. [Connecting to NESP 5.9 datasets](#connecting-to-nesp-59-datasets)\n",
+ "2. [H3 Aggregation and Mapping](#h3-aggregation-and-mapping)\n",
+ "3. [Identifying Trend Sites](#identifying-trend-sites)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## NESP 5.9 Datasets\n",
+ "\n",
+ "| Dataset name | Description | Metadata | S3 URL |\n",
+ "| ------------ | ----------- | -------- | ------ |\n",
+ "| AMSA | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/amsa/) |\n",
+ "| Kelp | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/kelp/kelp.parquet) |\n",
+ "| NRMN | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/nrmn/nrmn.parquet) |\n",
+ "| Seabird | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seabird/seabird.parquet) |\n",
+ "| Seagrass | TODO | TODO | [link](s3://data-uplift-public/stored/datauplift/seagrass/seagrass.parquet) |\n",
+ "\n",
+ "### S3 Details\n",
+ "NESP 5.9 datasets are currently stored in S3 and are publicly available as per the following table:\n",
+ "\n",
+ "| Bucket | Key | Partitioned |\n",
+ "| -------------------- | -------------------------------------------------------- | ----------- |\n",
+ "| `data-uplift-public` | `stored/datauplift/amsa/year=*/source=*/*.parquet` | [x] |\n",
+ "| `data-uplift-public` | `stored/datauplift/kelp/kelp.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/nrmn/nrmn.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seabird/seabird.parquet` | [ ] |\n",
+ "| `data-uplift-public` | `stored/datauplift/seagrass/seagrass.parquet` | [ ] |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Required Python & Packages\n",
+ "The required packages are found in the [pyproject.toml](pyproject.toml) under `dependencies`.\n",
+ "\n",
+ "### `uv` installation\n",
+ "It is highly recommended to manage python version, environment and projects with [uv](https://github.com/astral-sh/uv).\n",
+ "\n",
+ "```bash\n",
+ "uv venv && source .venv/bin/activate && uv pip install .\n",
+ "```\n",
+ "\n",
+ "### `pip` installation\n",
+ "Otherwise, native python tooling can be used to manage the python environment and project. \n",
+ "\n",
+ "*Note that python3.12 or greater is required.*\n",
+ "\n",
+ "```bash\n",
+ "python -m venv .venv && source .venv/bin/activate && pip install .\n",
+ "```\n",
+ "\n",
+ "### Running Notebooks in Jupyter or IDE\n",
+ "The notebook can be manually run by launching jupyter notebook from the console:\n",
+ "```bash\n",
+ "jupyter notebook\n",
+ "```\n",
+ "Otherwise, if running in an IDE, select the newly created `.venv` as the kernal.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## Notebook Stack\n",
+ "\n",
+ "### IO\n",
+ "`pyarrow` is the defacto parquet and arrow standard library tooling in the python eco-system, offering exceptional performance, documentation and stability.\n",
+ "\n",
+ "Its `fs` class allows connecting to parquet datasets in AWS S3, which is perfect for our use case.\n",
+ "\n",
+ "### Computation\n",
+ "`polars` is fast replacing pandas as the primary python dataframe API. It is more tightly integrated with arrow (and pyarrow), has exceptional performance, greater than memory dataset manipulation, a more consistent and declarative API, built in plotting and many other advantages.\n",
+ "\n",
+ "### Mapping\n",
+ "`pydeck` is a python binding for `deck.gl`, a GPU-powered framework for visual exploratory data analysis of large datasets. It is optimised for Jupyter notebooks and highly customisable.\n",
+ "\n",
+ "Standard mapping tools like `folium` struggle to handle the number of mapped shapes, so we favor the more optimised `pydeck` library."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Connecting to NESP 5.9 Datasets\n",
+ "\n",
+ "NESP 5.9 datasets can be connected to via a pyarrow `S3FileSystem`.\n",
+ "\n",
+ "The `S3FileSystem` manages the connection to the NESP 5.9 datasets stored in S3, resulting in a `pyarrow.Dataset` handle that we may use for efficient loading and subsetting of the dataset.\n",
+ "\n",
+ "Note that a dataset can be comprised of a single parquet file or partitioned (into separate parquet fragments/files).\n",
+ "\n",
+ "This step also makes the dataset schema available and file structure available allowing for pre-filtering without downloading the entire dataset.\n",
+ "\n",
+ "Finally, we may estimate the in memory dataset size by sampling the dataset. If the dataset is small, we may use a `DataFrame` straight away. Otherwise if the dataset is too large, we must pre-aggregate and or filter using a `LazyFrame` before loading the dataset into memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyarrow\n",
+ "import pyarrow.fs\n",
+ "import pyarrow.dataset\n",
+ "import util\n",
+ "\n",
+ "# Construct the anonymous file system responsible for reading from the public S3 bucket\n",
+ "FILE_SYSTEM = pyarrow.fs.S3FileSystem(\n",
+ " region=\"ap-southeast-2\", \n",
+ " anonymous=True,\n",
+ ")\n",
+ "\n",
+ "# Create the dataset connection\n",
+ "# By convention, datasets are labelled `ds`\n",
+ "ds = pyarrow.dataset.dataset(\n",
+ " source=\"data-uplift-public/stored/datauplift/seabird/seabird.parquet\",\n",
+ " filesystem=FILE_SYSTEM,\n",
+ ")\n",
+ "\n",
+ "# The comprising files of the dataset can be inspected\n",
+ "print(list(ds.get_fragments()))\n",
+ "\n",
+ "# The schema of the dataset can be inspected\n",
+ "util.print_schema_rich_table(\n",
+ " schema=ds.schema,\n",
+ " metadata_keys=[\"definition\", \"units\", \"long_name\", \"resolution\"]\n",
+ ")\n",
+ "\n",
+ "# For the full table scheme use native pyarrow representation\n",
+ "# rich.print(ds.schema)\n",
+ "\n",
+ "# Estimate dataset size\n",
+ "estimated_megabytes = util.estimate_dataset_size(\n",
+ " ds=ds,\n",
+ " n_samples=10_000,\n",
+ ")\n",
+ "print(f\"DS estimated size: {round(estimated_megabytes, 2)}MB\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "## Loading Data\n",
+ "The dataset handle is our connection to data in S3. We can use this handle to load data directly into a `DataFrame` or `LazyFrame`\n",
+ "\n",
+ "### DataFrame\n",
+ "For smaller datasets the `DataFrame` is preferrable. This loads the entire dataset into memory ready for analysis.\n",
+ "\n",
+ "### LazyFrame\n",
+ "For larger datasets the `DataFrame` may not fit into memory. In these cases the `LazyFrame` is ideal as it allows the dataset to be filtered and or aggregated prior to loading the dataset, allowing it to fit into memory, ready for analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import polars\n",
+ "import polars_h3\n",
+ "\n",
+ "# Option (a): Load entire dataset in memory as a dataframe\n",
+ "# Suitable for smaller datasets that fit into memory\n",
+ "df = polars.DataFrame(\n",
+ " data=ds.to_table(),\n",
+ ")\n",
+ "\n",
+ "# Option (b): Scan entire dataset as a lazyframe\n",
+ "# Suitable for larger datasets that don't fit into memory\n",
+ "lf = polars.scan_pyarrow_dataset(\n",
+ " source=ds,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "## Observation Effort, Organism Quantity and Richness\n",
+ "\n",
+ "### Observation Effort and Organism Quantity\n",
+ "**Observation Effort** is the number of occurrence records while **Organism Quantity** is the recorded count of a species occurrence.\n",
+ "\n",
+ "**Observation Effort** must be taken into account when analysing trends; fluctuations in observation effort tend to distort the reported occurrences.\n",
+ "\n",
+ "### Richness\n",
+ "Species **Richness** refers to the count of different species present in a particular area. While Organism Quantity tells us \"how many\" individuals there are, **Richness** tells us \"how many types\" of organisms coexist.\n",
+ "\n",
+ "Typically the **Richness** of an area correlates to the number of studies but eventually plateus and this plateu is an indication that the studied region is well understood."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import altair\n",
+ "\n",
+ "x = altair.X(\n",
+ " \"year:Q\", \n",
+ " title=\"Year\",\n",
+ ")\n",
+ "\n",
+ "yearly_stat_df = (\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"datasetName\").unique().alias(\"datasets\"),\n",
+ " polars.len().alias(\"nRecords\"),\n",
+ " polars.col(\"organismQuantity\").replace({\"present\": \"1\"}).cast(polars.Int64).sum()\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasets\").list.join(\", \"),\n",
+ " polars.col(\"datasets\").list.len().alias(\"nDatasets\"),\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"year\"),\n",
+ " )\n",
+ ")\n",
+ "dataset = (\n",
+ " altair.Chart(\n",
+ " data=yearly_stat_df,\n",
+ " title=altair.Title(\n",
+ " \"Dataset Quantity Over Time\",\n",
+ " subtitle=\"The number of datasets fluctuates over the aggregated dataset\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=altair.Y(\"nDatasets:Q\"),\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ ")\n",
+ "observation = (\n",
+ " altair.Chart(\n",
+ " data=yearly_stat_df,\n",
+ " title=altair.Title(\n",
+ " \"Observations Over Time\",\n",
+ " subtitle=\"Observation effort fluctuates over the aggregated dataset and is correlated to the number of underlying datasets\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=\"nRecords:Q\",\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ ")\n",
+ "quantity = (\n",
+ " altair.Chart(\n",
+ " data=yearly_stat_df,\n",
+ " title=altair.Title(\n",
+ " \"Organism Quantity Over Time\",\n",
+ " subtitle=\"Organism quantity similarly fluctuates over the aggregated dataset and is correlated to the number of underlying datasets\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=altair.Y(\"organismQuantity:Q\"),\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ ")\n",
+ "richness_df = (\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"scientificNameID\").str.split(\"urn:lsid:marinespecies.org:taxname:\").list.last()\n",
+ " )\n",
+ " .agg(\n",
+ " polars.struct(\n",
+ " polars.col(\"eventDate\").dt.year().min().alias(\"year\"),\n",
+ " polars.col(\"datasetName\").alias(\"dataset\"),\n",
+ " ).first().alias(\"year_dataset\"),\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"year_dataset\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"year_dataset\").struct.field(\"year\")\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"year_dataset\").struct.field(\"dataset\").unique().alias(\"datasets\"),\n",
+ " polars.col(\"scientificNameID\").count().alias(\"nSpecies\"),\n",
+ " polars.col(\"scientificNameID\").unique(),\n",
+ " )\n",
+ " .sort(\"year\")\n",
+ " .with_columns(\n",
+ " polars.col(\"scientificNameID\").list.join(\", \"),\n",
+ " polars.col(\"datasets\").list.join(\", \"),\n",
+ " polars.col(\"nSpecies\").cum_sum().alias(\"richness\"),\n",
+ " )\n",
+ ")\n",
+ "richness = (\n",
+ " altair.Chart(\n",
+ " data=richness_df,\n",
+ " title=altair.Title(\n",
+ " \"Species Richness Over Time\",\n",
+ " subtitle=\"Species richness has not yet plateued\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=500\n",
+ " )\n",
+ " .mark_area(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=altair.Y(\"richness\").title(\"Richness\"),\n",
+ " tooltip=[\"year\", \"richness\", \"datasets\", \"nSpecies\", \"scientificNameID\"],\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "altair.vconcat(\n",
+ " dataset,\n",
+ " observation,\n",
+ " quantity,\n",
+ " richness,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mesoscale_water_type_df = (\n",
+ " df\n",
+ " .with_columns(\n",
+ " polars.col(\"australianMarineRegionsTags\")\n",
+ " .str.split(\"|\")\n",
+ " .list.filter(\n",
+ " polars.element().str.contains(\"IMCRA:Mesoscale:Water Type\")\n",
+ " )\n",
+ " )\n",
+ " .filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").is_not_null(),\n",
+ " )\n",
+ " .filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").list.len().gt(0),\n",
+ " )\n",
+ " .explode(\n",
+ " polars.col(\"australianMarineRegionsTags\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"australianMarineRegionsTags\")\n",
+ " .str.strip_prefix(\"IMCRA:Mesoscale:Water Type:\")\n",
+ " .alias(\"mesoscaleWaterType\"),\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"eventDate\"),\n",
+ " )\n",
+ ")\n",
+ "mesoscale_water_type_yearly_stat_df = (\n",
+ " mesoscale_water_type_df\n",
+ " .group_by(\n",
+ " polars.col(\"eventDate\").dt.year().alias(\"year\"),\n",
+ " polars.col(\"mesoscaleWaterType\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"datasetName\").unique().alias(\"datasets\"),\n",
+ " polars.len().alias(\"nRecords\"),\n",
+ " polars.col(\"organismQuantity\").replace({\"present\": \"1\"}).cast(polars.Int64).sum()\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"year\"),\n",
+ " polars.col(\"mesoscaleWaterType\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasets\").list.join(\", \"),\n",
+ " polars.col(\"datasets\").list.len().alias(\"nDatasets\"),\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "mesoscale_water_type_richness_df = (\n",
+ " mesoscale_water_type_df\n",
+ " .group_by(\n",
+ " polars.col(\"scientificNameID\").str.split(\"urn:lsid:marinespecies.org:taxname:\").list.last(),\n",
+ " polars.col(\"mesoscaleWaterType\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.struct(\n",
+ " polars.col(\"eventDate\").dt.year().min().alias(\"year\"),\n",
+ " polars.col(\"datasetName\").alias(\"dataset\"),\n",
+ " ).first().alias(\"year_dataset\"),\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"year_dataset\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"year_dataset\").struct.field(\"year\"),\n",
+ " polars.col(\"mesoscaleWaterType\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"year_dataset\").struct.field(\"dataset\").unique().alias(\"datasets\"),\n",
+ " polars.col(\"scientificNameID\").count().alias(\"nSpecies\"),\n",
+ " polars.col(\"scientificNameID\").unique(),\n",
+ " )\n",
+ " .sort(\"year\")\n",
+ " .with_columns(\n",
+ " polars.col(\"scientificNameID\").list.join(\", \"),\n",
+ " polars.col(\"datasets\").list.join(\", \"),\n",
+ " polars.col(\"nSpecies\").cum_sum().over(\"mesoscaleWaterType\").alias(\"richness\"),\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "domain = (\n",
+ " mesoscale_water_type_yearly_stat_df[\"year\"].min(),\n",
+ " mesoscale_water_type_yearly_stat_df[\"year\"].max(),\n",
+ ")\n",
+ "x = altair.X(\n",
+ " \"year:Q\", \n",
+ " title=\"Year\",\n",
+ ").scale(domain=domain)\n",
+ "\n",
+ "for mesoscaleWaterType in mesoscale_water_type_yearly_stat_df[\"mesoscaleWaterType\"].unique().sort():\n",
+ "\n",
+ " dataset = (\n",
+ " altair.Chart(\n",
+ " data=mesoscale_water_type_yearly_stat_df.filter(\n",
+ " polars.col(\"mesoscaleWaterType\").eq(mesoscaleWaterType),\n",
+ " ),\n",
+ " title=altair.Title(\n",
+ " \"Dataset Quantity Over Time\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=altair.Y(\"nDatasets:Q\"),\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " observation = (\n",
+ " altair.Chart(\n",
+ " data=mesoscale_water_type_yearly_stat_df.filter(\n",
+ " polars.col(\"mesoscaleWaterType\").eq(mesoscaleWaterType),\n",
+ " ),\n",
+ " title=altair.Title(\n",
+ " \"Observations Over Time\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=\"nRecords:Q\",\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " quantity = (\n",
+ " altair.Chart(\n",
+ " data=mesoscale_water_type_yearly_stat_df.filter(\n",
+ " polars.col(\"mesoscaleWaterType\").eq(mesoscaleWaterType),\n",
+ " ),\n",
+ " title=altair.Title(\n",
+ " \"Organism Quantity Over Time\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=200\n",
+ " )\n",
+ " .mark_bar(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=x,\n",
+ " y=altair.Y(\"organismQuantity:Q\"),\n",
+ " tooltip=[\"year\", \"datasets\", \"nRecords\"],\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " richness = (\n",
+ " altair.Chart(\n",
+ " data=mesoscale_water_type_richness_df.filter(\n",
+ " polars.col(\"mesoscaleWaterType\").eq(mesoscaleWaterType),\n",
+ " ),\n",
+ " title=altair.Title(\n",
+ " \"Species Richness Over Time\",\n",
+ " anchor=\"start\",\n",
+ " orient=\"top\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " ),\n",
+ " width=1000,\n",
+ " height=500\n",
+ " )\n",
+ " .mark_area(\n",
+ " point=True,\n",
+ " interpolate=\"monotone\",\n",
+ " )\n",
+ " .encode(\n",
+ " x=altair.X(\"year\").title(\"Year\"),\n",
+ " y=altair.Y(\"richness\").title(\"Richness\"),\n",
+ " tooltip=[\"year\", \"richness\", \"datasets\", \"nSpecies\", \"scientificNameID\"],\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " altair.vconcat(\n",
+ " dataset,\n",
+ " observation,\n",
+ " quantity,\n",
+ " richness,\n",
+ " ).properties(\n",
+ " title=altair.Title(\n",
+ " text=mesoscaleWaterType,\n",
+ " fontSize=20,\n",
+ " anchor=\"start\",\n",
+ " offset=20,\n",
+ " frame=\"group\",\n",
+ " \n",
+ " ),\n",
+ " ).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "## H3 Aggregation and Mapping\n",
+ "The NESP 5.9 datasets are aggregated data products that cover large regions and time periods.\n",
+ "\n",
+ "It is useful to first aggregate the datasets so that we may understand their temporal-spatial bounds.\n",
+ "\n",
+ "### H3 Indexing\n",
+ "The `h3` library allows us to spatially index datasets at different [resolutions](https://h3geo.org/docs/core-library/restable#average-area-in-km2) (zoom levels) according to the record latitude and longitude.\n",
+ "\n",
+ "Additionally, the `polars_h3` library offers a polars style interface for faster tagging with `polars.DataFrames`:\n",
+ "```python\n",
+ "\n",
+ "# Add a h3 index to any dataframe with latitude and longitude columns\n",
+ "df = df.with_columns(\n",
+ "\n",
+ " # Polars Expression Shorthand to access the `polars_h3` namespace\n",
+ " polars_h3.latlng_to_cell(\n",
+ "\n",
+ " # Specify the latitude and longitude columns\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ "\n",
+ " # Specify the resolution\n",
+ " resolution=5,\n",
+ "\n",
+ " # Specify the index return type, can be `polars.Int64` or `polars.String`\n",
+ " return_dtype=polars.String,\n",
+ " )\n",
+ " \n",
+ " # Alias to `h3Index`\n",
+ " .alias(\"h3Index\"),\n",
+ ")\n",
+ "```\n",
+ "\n",
+ "### H3 Mapping\n",
+ "The `pydeck` library offers a built in [`H3HexagonLayer`](https://deck.gl/docs/api-reference/geo-layers/h3-hexagon-layer) layer type that greatly reduces complexity in mapping H3 Shapes:\n",
+ "\n",
+ "```python\n",
+ "\n",
+ "# Generate a h3 hexagon layer displayable on a pydeck map\n",
+ "pydeck.Layer(\n",
+ "\n",
+ " # Specify this is a H3 Hexagon Layer\n",
+ " \"H3HexagonLayer\",\n",
+ "\n",
+ " # Unfortunately no polars dataframe support, so cast to `pandas.DataFrame`\n",
+ " df.to_pandas(use_pyarrow_extension_array=True),\n",
+ "\n",
+ " # Specify the hexagon\n",
+ " get_hexagon=\"h3Index\",\n",
+ "\n",
+ ")\n",
+ "```\n",
+ "This is rendered with a [`pydeck.Deck`](https://deck.gl/docs/api-reference/core/deck):\n",
+ "```python\n",
+ "\n",
+ "# Generate a deck with h3 layers\n",
+ "deck = pydeck.Deck(\n",
+ "\n",
+ " # Supply the h3 layers\n",
+ " layers=h3_layers,\n",
+ ")\n",
+ "deck.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.len().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " .sort(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydeck\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ "\n",
+ " # H3 Aggregated dataframe with dataset and number of records attributes\n",
+ " df\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.GLOBAL_VIEW_STATE,\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15",
+ "metadata": {},
+ "source": [
+ "## Australian Marine Regions\n",
+ "Each occurrence has been tagged by the following Australian Marine Regions (see table below). Note that the taggin is not 100% accurate at the region boundaries; it is accurate to ~6km.\n",
+ "\n",
+ "Note that the Great Barrier Reef has been included from the World Heritage Areas map.\n",
+ "\n",
+ "| Marine Region Type | Map Explore URL | Metadata URL (REST/About) | GeoJSON API Endpoint |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| **Commonwealth Marine Regions** | [Explore](https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Commonwealth_Marine_Regions/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **World Heritage Areas** | [Explore](https://fed.dcceew.gov.au/datasets/erin::australia-world-heritage-areas/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/World_Heritage_Areas/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **Marine Parks** | [Explore](https://fed.dcceew.gov.au/datasets/2b3eb1d42b8d4319900cf4777f0a83b9_0/explore) | [Metadata](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/Australian_Marine_Parks/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Provincial** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-provincial-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Provincial/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "| **IMCRA Mesoscale** | [Explore](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/explore) | [Metadata](https://fed.dcceew.gov.au/datasets/erin::integrated-marine-and-coastal-regionalisation-of-australia-imcra-v4-0-meso-scale-bioregions/about) | [GeoJSON](https://gis.environment.gov.au/gispubmap/rest/services/ogc_services/IMCRA_Mesoscale/MapServer/0/query?outFields=*&where=1%3D1&f=geojson) |\n",
+ "\n",
+ "Follow the **Explore** links to explore the Australian Marine Regions. The Commonwealth Marine Regions explore map has been embedded below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import IPython.display\n",
+ "\n",
+ "# Display the Commonwealth Marine Regions\n",
+ "IPython.display.IFrame(\n",
+ " src=\"https://fed.dcceew.gov.au/datasets/commonwealth-marine-regions/explore?location=-33.665700%2C140.517300%2C4\",\n",
+ " width=\"100%\", \n",
+ " height=\"1000px\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "### Exploring Available Marine Regions Tags\n",
+ "\n",
+ "Australian Marine Regions Tags have the following syntax:\n",
+ "\n",
+ "| TAG | TYPE | OPTIONAL SUPER REGIONS | REGION |\n",
+ "| :--- | :--- | :--- | :--- |\n",
+ "| `Commonwealth Marine Region: South-east` | `Commonwealth Marine Region` | | `South-east` |\n",
+ "| `Marine Park:National Park Zone:Freycinet` | `Marine Park` | `National Park Zone` | `Freycinet` |\n",
+ "| `IMCRA:Provincial:Water Type:Cold Temperate Waters` | `IMCRA` | `Provincial`, `Water Type` | `Cold Temperate Waters` |\n",
+ "\n",
+ "Extracting the available Australian Marine Regions Tags requires spliting the delimited column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract the unique tags\n",
+ "australian_marine_regions_tags = df[\"australianMarineRegionsTags\"].str.split(\"|\").explode().value_counts(sort=True).drop_nulls()\n",
+ "\n",
+ "# Maniuplate polars display config to display full tag names\n",
+ "with polars.Config(\n",
+ " fmt_str_lengths=128,\n",
+ " tbl_rows=20\n",
+ "):\n",
+ " display(australian_marine_regions_tags)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "### Filtering to All Data within Australian Marine Regions\n",
+ "We may filter to a particular requires a using not `is_null` constraint:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the commonwealth marine regions\n",
+ "australian_marine_regions_df = df.filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").is_null().not_()\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " australian_marine_regions_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=5,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " polars.col(\"australianMarineRegionsTags\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " # De-duplicate marine tags\n",
+ " (\n",
+ " polars.col(\"australianMarineRegionsTags\")\n",
+ " # Flatten \"|\" separated list of strings into single wide \"|\" separated str\n",
+ " .list.unique().list.join(\"|\")\n",
+ " # Split and filter to sorted uniques of wide \"|\" separated str\n",
+ " .str.split(\"|\").list.unique().list.sort().list.join(\"|\").alias(\"tags\")\n",
+ " ),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip={\n",
+ " \"html\": \"\"\"\n",
+ " \n",
+ " H3 Index: {h3Index}
\n",
+ "
\n",
+ " Records: {n_records}
\n",
+ " Datasets: {datasets}
\n",
+ " Tags: {tags}
\n",
+ " \n",
+ " \"\"\",\n",
+ " \"style\": {\n",
+ " \"width\": \"33%\",\n",
+ " \"backgroundColor\": \"#2b2b2b\",\n",
+ " \"color\": \"white\",\n",
+ " \"borderRadius\": \"4px\",\n",
+ " \"zIndex\": \"1000\"\n",
+ " }\n",
+ " },\n",
+ " initial_view_state=util.generate_view_state(australian_marine_regions_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21",
+ "metadata": {},
+ "source": [
+ "### Filtering to a Particular Marine Region\n",
+ "We may filter to a particular requires a using `str.contains` expression:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the commonwealth marine regions\n",
+ "south_east_commonwealth_marine_region_df = df.filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").str.contains(\n",
+ " pattern=\"Commonwealth Marine Region:South-east\",\n",
+ " literal=True,\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " south_east_commonwealth_marine_region_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=5,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(south_east_commonwealth_marine_region_df),\n",
+ " height=1000,\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23",
+ "metadata": {},
+ "source": [
+ "## Identifying Trend Sites\n",
+ "In the global map we can see there are some sites with a high density of records. These are the most ideal sites for trend analysis.\n",
+ "\n",
+ "### Per Region\n",
+ "[Regions](#australian-marine-regions) with a high density of records are typically good candidates for trend analysis. See above for subsetting and plotting by [Australian Marine Regions](#australian-marine-regions).\n",
+ "\n",
+ "### Per Dataset\n",
+ "Additionally, the singular datasets that comprise the aggregated data product are typically good candidates for trend analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24",
+ "metadata": {},
+ "source": [
+ "### Mapping the Spatial Extent of a Particular Dataset\n",
+ "Similarly to mapping Australian Marine Regions, we may map particular datasets by applying a string constraint.\n",
+ "\n",
+ "#### Finding Available Datasets\n",
+ "We may find available datasets by aggregating by the `datasetName` column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[\"datasetName\"].value_counts(sort=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to the `seabird atlas sea` dataset\n",
+ "seabird_atlas_sea_df = df.filter(\n",
+ " polars.col(\"datasetName\").eq(\"seabird_atlas_southeast_australia\"),\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " seabird_atlas_sea_df\n",
+ "\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(df=seabird_atlas_sea_df)\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27",
+ "metadata": {},
+ "source": [
+ "## Wind Farm Regions\n",
+ "Bird and vessel traffic are of signifcant interest to offshore wind farms; generally, it is desireable to minimise the impact of offshore wind farms on human and wildlife activities.\n",
+ "\n",
+ "### H3 Index Joins\n",
+ "Additional to pre-processing, H3 indexes may be applied to new geometries.\n",
+ "\n",
+ "Note that re-indexing to the same H3 resolution may be required (and is so in the following case)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pathlib\n",
+ "import polars_st\n",
+ "import json\n",
+ "\n",
+ "gdf = util.generate_geodataframe_from_shapefile(pathlib.Path(\"OffshoreRenewable_Energy_Infrastructure_Regions.zip\"))\n",
+ "\n",
+ "# Create DataFrame\n",
+ "offshore_wind_gdf = polars_st.GeoDataFrame(pyarrow.table(gdf.to_arrow()))\n",
+ "offshore_wind_gdf = (\n",
+ " offshore_wind_gdf\n",
+ " .filter(\n",
+ " polars.col(\"Status\").eq(\"Declared\"),\n",
+ " )\n",
+ " .select(\n",
+ " polars.concat_str(\n",
+ " polars.col(\"Region\"),\n",
+ " separator=\":\",\n",
+ " ).alias(\"tag\"),\n",
+ " polars.col(\"geometry\"),\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Create GeoJson\n",
+ "offshore_wind_gdf_geo_json = gdf[gdf[\"Status\"] == \"Declared\"][[\"geometry\", \"Region\", \"Name\", \"Status\"]].to_json()\n",
+ "offshore_wind_layers = [\n",
+ " pydeck.Layer(\n",
+ " \"GeoJsonLayer\",\n",
+ " json.loads(offshore_wind_gdf_geo_json),\n",
+ " get_fill_color=[0, 0, 255, 255/2],\n",
+ " filled=True,\n",
+ " pickable=True,\n",
+ " auto_highlight=True,\n",
+ " )\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Subset to marine regions intersecting the offshore wind farms\n",
+ "offshore_wind_df = (\n",
+ " df\n",
+ " .filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").str.contains(\n",
+ " pattern=(\n",
+ " \"South-east\"\n",
+ " \"|Temperate East\"\n",
+ " \"|South-west\"\n",
+ " )\n",
+ " ),\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "h3_indexed_offshore_wind_df = util.generate_wkt_to_h3_index(\n",
+ " gdf=offshore_wind_gdf,\n",
+ " h3_resolution=6,\n",
+ ")\n",
+ "h3_indexed_offshore_wind_df.sort(\n",
+ " polars.col(\"n_tags\"),\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=(\n",
+ " offshore_wind_df\n",
+ " .filter(\n",
+ " polars.col(\"australianMarineRegionsTags\").is_null().not_()\n",
+ " )\n",
+ " # Additional re-index at a higher resolution\n",
+ " .with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " )\n",
+ " .group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " )\n",
+ " .agg(\n",
+ " polars.col(\"id\").count().alias(\"n_records\"),\n",
+ " polars.col(\"datasetName\"),\n",
+ " )\n",
+ " .with_columns(\n",
+ " polars.col(\"datasetName\").list.unique().list.sort().list.join(\", \").alias(\"datasets\"),\n",
+ " )\n",
+ " ),\n",
+ " aggregate_column_name=\"n_records\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = h3_layers\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=h3_layers + offshore_wind_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " initial_view_state=util.generate_view_state(\n",
+ " df=h3_indexed_offshore_wind_df.with_columns(\n",
+ " polars_h3.cell_to_lat(polars.col(\"h3Index\")).alias(\"decimalLatitude\"),\n",
+ " polars_h3.cell_to_lng(polars.col(\"h3Index\")).alias(\"decimalLongitude\"),\n",
+ " ),\n",
+ " ),\n",
+ ")\n",
+ "deck.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter to declared offshore wind regions that intersect occurrences\n",
+ "# This uses a H3 index join\n",
+ "offshore_wind_seabird_occurrences_df = (\n",
+ " df.with_columns(\n",
+ " polars_h3.latlng_to_cell(\n",
+ " lat=polars.col(\"decimalLatitude\"),\n",
+ " lng=polars.col(\"decimalLongitude\"),\n",
+ " resolution=6,\n",
+ " return_dtype=polars.String,\n",
+ " ).alias(\"h3Index\"),\n",
+ " ).join(\n",
+ " other=h3_indexed_offshore_wind_df,\n",
+ " on=\"h3Index\",\n",
+ " validate=\"m:1\",\n",
+ " )\n",
+ ")\n",
+ "offshore_wind_seabird_occurrences_df\n",
+ "\n",
+ "# Aggregate and generate h3 layers\n",
+ "h3_layers = util.generate_pydeck_hexagon_layers(\n",
+ " df=offshore_wind_seabird_occurrences_df.group_by(\n",
+ " polars.col(\"h3Index\"),\n",
+ " ).agg(\n",
+ " polars.col(\"scientificName\"),\n",
+ " polars.col(\"scientificNameID\"),\n",
+ " ).with_columns(\n",
+ " polars.col(\"scientificName\").list.unique().list.join(\"|\"),\n",
+ " polars.col(\"scientificNameID\").list.unique().list.len().alias(\"nSpecies\"),\n",
+ " ),\n",
+ " aggregate_column_name=\"nSpecies\",\n",
+ " n_quantiles = 10,\n",
+ " color_palette=\"plasma\",\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# Run the map\n",
+ "deck = pydeck.Deck(\n",
+ " layers=offshore_wind_layers + h3_layers,\n",
+ " map_style=pydeck.map_styles.LIGHT_NO_LABELS,\n",
+ " # tooltip=util.TOOLTIP,\n",
+ " initial_view_state=util.generate_view_state(offshore_wind_seabird_occurrences_df),\n",
+ ")\n",
+ "deck.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "formats": "ipynb,py:percent"
+ },
+ "kernelspec": {
+ "display_name": "NESP",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NESP/util.py b/NESP/util.py
new file mode 100644
index 0000000..9f20ec4
--- /dev/null
+++ b/NESP/util.py
@@ -0,0 +1,342 @@
+"""
+Utility functions for NESP data processing and visualization.
+
+This module provides helper functions for:
+- Generating color mappings and hexbin visualizations using pydeck
+- Creating rich tables for schema display
+- Estimating pyarrow dataset sizes
+- Generating PyDeck Artifacts
+"""
+import matplotlib.pyplot
+import pyarrow
+import pyarrow.dataset
+import rich.console
+import rich.table
+import typing
+import polars
+import pydeck
+import pydeck.data_utils
+import tempfile
+import pathlib
+import zipfile
+import geopandas
+import h3
+import polars_st
+
+N_QUANTILES = 10
+COLOR_PALETTE = "plasma"
+COLOR_PALETTE_LITERAL = typing.Literal["viridis", "plasma", "inferno", "magma", "cividis"]
+TOOLTIP = {
+ "html": """
+
+ H3 Index: {h3Index}
+
+ Records: {n_records}
+ Datasets: {datasets}
+
+ """,
+ "style": {
+ "width": "33%",
+ "backgroundColor": "#2b2b2b",
+ "color": "white",
+ "borderRadius": "4px",
+ "zIndex": "1000"
+ }
+}
+GLOBAL_VIEW_STATE = pydeck.ViewState(
+ latitude=0,
+ longitude=0,
+ zoom=0.75,
+ pitch=0,
+ bearing=0,
+)
+
+
+def generate_color_mapping(
+ n_quantiles: int = N_QUANTILES,
+ color_palette: COLOR_PALETTE_LITERAL = COLOR_PALETTE,
+ alpha: float = 0.8,
+) -> dict[str, list[int]]:
+ """
+ Generate a mapping from quantile indices to RGBA color values.
+
+ :param n_quantiles: Number of quantiles/colors to generate.
+ :type n_quantiles: int
+ :param color_palette: Name of the matplotlib color palette to use.
+ :type color_palette: COLOR_PALETTE_LITERAL
+ :param alpha: Alpha value for color transparency (0.0-1.0).
+ :type alpha: float
+ :return: Dictionary mapping quantile indices (as strings) to RGBA color lists.
+ :rtype: dict[str, list[int]]
+ """
+
+ # Sample colours
+ colors = matplotlib.pyplot.get_cmap(color_palette)([i / (n_quantiles - 1) for i in range(n_quantiles)])
+
+ # Convert from 0.0-1.0 floats to 0-255 integers
+ mapping = {
+ str(i): [int(r * 255), int(g * 255), int(b * 255), int(alpha * 255)]
+ for i, (r, g, b, _) in enumerate(colors)
+ }
+
+ return mapping
+
+def generate_color_index_series(
+ df: polars.DataFrame,
+ aggregate_column_name: str,
+ color_mapping: dict[str, list[int]] = generate_color_mapping(
+ n_quantiles=N_QUANTILES,
+ color_palette=COLOR_PALETTE,
+ ),
+) -> polars.Series:
+ """
+ Generate a polars Series assigning each row to a color index based on quantiles.
+
+ :param df: Input polars DataFrame.
+ :type df: polars.DataFrame
+ :param aggregate_column_name: Name of the column to aggregate and bin.
+ :type aggregate_column_name: str
+ :param color_mapping: Mapping of color indices to RGBA values.
+ :type color_mapping: dict[str, list[int]]
+ :return: Series of color indices as strings.
+ :rtype: polars.Series
+ """
+
+ # Bin the h3 cells
+ return df[aggregate_column_name].qcut(
+ quantiles=len(color_mapping),
+ labels=list(color_mapping.keys()),
+ allow_duplicates=True,
+ ).cast(polars.String)
+
+def generate_pydeck_hexagon_layers(
+ df: polars.DataFrame,
+ aggregate_column_name: str,
+ hexagon_index_column_name: str = "h3Index",
+ n_quantiles: int = N_QUANTILES,
+ color_palette: COLOR_PALETTE_LITERAL = COLOR_PALETTE,
+) -> list[pydeck.Layer]:
+ """
+ Generate a list of pydeck H3HexagonLayer objects for visualizing hexagons.
+
+ :param df: Input polars DataFrame.
+ :type df: polars.DataFrame
+ :param aggregate_column_name: Column to aggregate for coloring.
+ :type aggregate_column_name: str
+ :param hexagon_index_column_name: Column containing H3 hexagon indices.
+ :type hexagon_index_column_name: str
+ :param n_quantiles: Number of quantiles/colors.
+ :type n_quantiles: int
+ :param color_palette: Name of the matplotlib color palette.
+ :type color_palette: COLOR_PALETTE_LITERAL
+ :return: List of pydeck Layer objects.
+ :rtype: list[pydeck.Layer]
+ """
+
+ # Generate color mapping
+ color_mapping = generate_color_mapping(
+ n_quantiles=n_quantiles,
+ color_palette=color_palette,
+ )
+
+ # Add a color index to the df
+ color_index_column_name = "__color_index__"
+ df = df.with_columns(
+ generate_color_index_series(
+ df=df,
+ aggregate_column_name=aggregate_column_name,
+ color_mapping=color_mapping,
+ ).alias(color_index_column_name),
+ )
+
+ return [
+ pydeck.Layer(
+ "H3HexagonLayer",
+ df.filter(
+ polars.col(color_index_column_name).eq(color_index),
+ ).to_pandas(use_pyarrow_extension_array=True),
+ get_hexagon=hexagon_index_column_name,
+ auto_highlight=True,
+ extruded=False,
+ get_fill_color=fill_color,
+ get_line_color=fill_color[:3],
+ line_width_min_pixels=2,
+ pickable=True,
+ )
+ for color_index, fill_color in color_mapping.items()
+ ]
+
+def print_schema_rich_table(
+ schema: pyarrow.Schema,
+ metadata_keys: list[str] = ["definition", "units"]
+) -> None:
+ """
+ Generate a rich Table displaying schema field names and metadata.
+
+ :param schema: PyArrow schema object.
+ :type schema: pyarrow.Schema
+ :param metadata_keys: List of metadata keys to display.
+ :type metadata_keys: list[str]
+ :return: Rich Table object with schema information.
+ :rtype: rich.table.Table
+ """
+
+ # Construct table
+ table = rich.table.Table()
+ table.add_column("name")
+ for metadata_key in metadata_keys:
+ table.add_column(metadata_key)
+
+ # Add the field name and definition rows
+ for field in schema:
+
+ # Add name
+ field_row = [
+ field.name,
+ ]
+
+ # Add additional metadata
+ for metadata_key in metadata_keys:
+ field_row.append(
+ field.metadata.get(metadata_key.encode(), b"").decode()
+ if field.metadata
+ else None
+ )
+ table.add_row(*field_row)
+ table.add_section()
+
+ rich.console.Console().print(table)
+
+def generate_view_state(
+ df: polars.DataFrame,
+ view_proportion: float = 1.0,
+ longitude_column_name: str = "decimalLongitude",
+ latitude_column_name: str = "decimalLatitude",
+) -> pydeck.ViewState:
+ """
+ Compute a pydeck ViewState centered and zoomed to fit the data points.
+
+ :param df: Input polars DataFrame containing longitude and latitude columns.
+ :type df: polars.DataFrame
+ :param view_proportion: Proportion of the view to use for fitting points (0.0-1.0).
+ :type view_proportion: float
+ :param longitude_column_name: Name of the longitude column.
+ :type longitude_column_name: str
+ :param latitude_column_name: Name of the latitude column.
+ :type latitude_column_name: str
+ :return: Computed pydeck ViewState object.
+ :rtype: pydeck.ViewState
+ """
+ return pydeck.data_utils.compute_view(
+ points=df.select(
+ polars.col(longitude_column_name),
+ polars.col(latitude_column_name),
+ ).to_numpy(),
+ view_proportion=view_proportion,
+ )
+
+def estimate_dataset_size(
+ ds: pyarrow.dataset.Dataset,
+ n_samples: int = 10_000,
+) -> int:
+ """
+ Estimate the size of a PyArrow dataset in megabytes by sampling rows.
+
+ :param ds: PyArrow dataset object.
+ :type ds: pyarrow.dataset.Dataset
+ :param n_samples: Number of rows to sample for estimation.
+ :type n_samples: int
+ :return: Estimated dataset size in megabytes.
+ :rtype: int
+ """
+
+ # Take a sample
+ n_rows = ds.count_rows()
+ sample= ds.head(min(n_samples, n_rows))
+
+ # Estimate dataset size
+ avg_bytes_per_row = sample.nbytes / min(n_samples, n_rows)
+ estimated_bytes = avg_bytes_per_row * n_rows
+ estimated_megabytes = estimated_bytes / (2 ** 20)
+ return estimated_megabytes
+
+
+def generate_geodataframe_from_shapefile(path: pathlib.Path) -> geopandas.GeoDataFrame:
+
+ # Unzip into a temporary directory
+ with tempfile.TemporaryDirectory() as temporary_directory:
+ # Unzip the path
+ with zipfile.ZipFile(path, 'r') as zip_ref:
+ zip_ref.extractall(temporary_directory)
+
+ # Read the shape file
+ gdf = geopandas.read_file(
+ temporary_directory,
+ engine="pyogrio",
+ )
+
+ # Check and translate to 4326
+ if gdf.crs is None or gdf.crs.to_epsg() != 4326:
+ gdf = gdf.to_crs("EPSG:4326")
+
+ return gdf
+
+def generate_wkt_to_h3_index(
+ gdf: polars_st.GeoDataFrame,
+ h3_resolution: int = 5,
+ tag_column_name: str = "tag",
+) -> polars.DataFrame:
+ """
+ Convert geometries in a GeoDataFrame to H3 hexagon indices and aggregate by tags.
+
+ This function takes a GeoDataFrame containing geometries, converts each geometry
+ to H3 hexagon indices at the specified resolution, and groups the results by
+ H3 index while aggregating associated tags.
+
+ :param gdf: Input GeoDataFrame containing geometries and tag information.
+ :type gdf: polars_st.GeoDataFrame
+ :param h3_resolution: H3 resolution level for hexagon indexing (0-15).
+ :type h3_resolution: int
+ :param tag_column_name: Name of the column containing tags to aggregate.
+ :type tag_column_name: str
+ :return: DataFrame with H3 indices, aggregated tags, and tag counts.
+ :rtype: polars.DataFrame
+ """
+ return (
+ gdf
+ .with_columns(
+ # Get h3 shapes per wkt
+ # Produces list[h3_index]
+ polars_st.geom().st.to_dict().map_elements(
+ function=lambda geo_interface: h3.h3shape_to_cells_experimental(
+ h3shape=h3.geo_to_h3shape(geo_interface),
+ res=h3_resolution,
+ contain="overlap",
+ ),
+ return_dtype=polars.List(polars.String)
+ ).alias("h3Index"),
+ # Add h3 resolution scalar
+ polars.lit(h3_resolution).cast(polars.Int8).alias("h3_resolution"),
+ )
+ # Drop no longer required geometry
+ .drop(
+ polars_st.geom(),
+ )
+ # Generate record per geometry h3 indexes
+ .explode(
+ polars.col("h3Index"),
+ )
+ # Group by the location
+ .group_by(
+ polars.col("h3_resolution"),
+ polars.col("h3Index")
+ ).agg(
+ polars.col(tag_column_name).alias("tags"),
+ ).with_columns(
+ # Aggregate tags to a representable string
+ polars.col("tags").list.join("|"),
+ polars.col("tags").list.len().alias("n_tags"),
+ ).sort(
+ polars.col("tags"),
+ )
+ )
\ No newline at end of file
diff --git a/NESP/uv.lock b/NESP/uv.lock
new file mode 100644
index 0000000..d110ade
--- /dev/null
+++ b/NESP/uv.lock
@@ -0,0 +1,2347 @@
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+]
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diff --git a/Python/templates/imos_Library_Template.ipynb b/Python/templates/imos_Library_Template.ipynb
index ff72b33..4bfef07 100644
--- a/Python/templates/imos_Library_Template.ipynb
+++ b/Python/templates/imos_Library_Template.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "b16dbdec-dd6d-4641-9970-f67348ba7bf9",
+ "id": "0",
"metadata": {},
"source": [
"\n",
@@ -23,7 +23,7 @@
},
{
"cell_type": "markdown",
- "id": "00a6b073-95aa-404f-a6ed-168040dc16fb",
+ "id": "1",
"metadata": {},
"source": [
"## Table of Contents\n",
@@ -44,25 +44,25 @@
},
{
"cell_type": "markdown",
- "id": "bef7433b-247d-4ad6-b1d2-144c85198865",
+ "id": "2",
"metadata": {},
"source": [
"\n",
"## Background\n",
"### What is this example about?\n",
"\n",
- "Breif description of science/product. In depth description of the use case, using references to illustrate (if needed). Indicate the expected output of this notebook (eg. a time series plot, a spatial aggregation of one variable, etc.) "
+ "Brief description of science/product. In depth description of the use case, using references to illustrate (if needed). Indicate the expected output of this notebook (eg. a time series plot, a spatial aggregation of one variable, etc.) "
]
},
{
"cell_type": "markdown",
- "id": "1f6a1f5f",
+ "id": "3",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
- "id": "56645fb2",
+ "id": "4",
"metadata": {},
"source": [
"## Datasets used in this notebook \n",
@@ -78,7 +78,7 @@
},
{
"cell_type": "markdown",
- "id": "205f5278-af23-4dc3-9299-741b977ca7f1",
+ "id": "5",
"metadata": {
"tags": []
},
@@ -92,7 +92,7 @@
"- EG: zarr==2.17.0\n",
"\n",
"Provide a link to a requirements.txt file if possible.\n",
- "Provide instructions on how to load libraryes from requirements.txt file if possible.\n",
+ "Provide instructions on how to load libraries from requirements.txt file if possible.\n",
"- EG: `pip install -r requirements.txt`\n",
"- EG: `conda create --name myenv --file requirements.txt`\n",
"- EG: `mamba create --name myenv --file requirements.txt`\n",
@@ -101,7 +101,7 @@
},
{
"cell_type": "markdown",
- "id": "virtual-projection",
+ "id": "6",
"metadata": {},
"source": [
"## 1. Step by Step guide \n",
@@ -111,8 +111,8 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "id": "6fb5a5bc-d91f-4214-af05-cf075a2b24ce",
+ "execution_count": null,
+ "id": "7",
"metadata": {},
"outputs": [],
"source": [
@@ -121,7 +121,7 @@
},
{
"cell_type": "markdown",
- "id": "threaded-strip",
+ "id": "8",
"metadata": {},
"source": [
"\n",
@@ -130,8 +130,8 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "id": "c8008699-a3a3-44bd-8606-4e02a20f6fdc",
+ "execution_count": null,
+ "id": "9",
"metadata": {},
"outputs": [],
"source": [
@@ -140,7 +140,7 @@
},
{
"cell_type": "markdown",
- "id": "banner-barcelona",
+ "id": "10",
"metadata": {},
"source": [
"## 2. Second Step \n"
@@ -148,8 +148,8 @@
},
{
"cell_type": "code",
- "execution_count": 26,
- "id": "b396dd9b-771b-49f0-a109-85b327fe35be",
+ "execution_count": null,
+ "id": "11",
"metadata": {},
"outputs": [],
"source": [
@@ -158,7 +158,7 @@
},
{
"cell_type": "markdown",
- "id": "searching-nursing",
+ "id": "12",
"metadata": {},
"source": [
"\n",
@@ -167,8 +167,8 @@
},
{
"cell_type": "code",
- "execution_count": 27,
- "id": "bbfedd0d-f0d7-4a55-847e-f5d7fecbe5af",
+ "execution_count": null,
+ "id": "13",
"metadata": {},
"outputs": [],
"source": [
@@ -177,7 +177,7 @@
},
{
"cell_type": "markdown",
- "id": "close-harbor",
+ "id": "14",
"metadata": {},
"source": [
"## 3. Exercise: \n",
@@ -187,8 +187,8 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "id": "78895465-11b6-487b-802a-daee821eda9a",
+ "execution_count": null,
+ "id": "15",
"metadata": {},
"outputs": [],
"source": [
@@ -197,7 +197,7 @@
},
{
"cell_type": "markdown",
- "id": "3e3c81ca",
+ "id": "16",
"metadata": {},
"source": [
"-------------------\n",